From 307bc19968af81f1bdeacee26d28ccc438bcbdc0 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Tue, 12 Aug 2025 16:11:56 -0700
Subject: [PATCH 01/11] Remove Feature Management FAQs

---
 .../10-getting-started/overview/boxes-demo.md |  69 +++++++++++
 .../overview/build-a-resilient-integration.md |   2 +-
 .../overview/create-a-metric-alert-policy.md  |   2 +-
 .../overview/create-a-metric.md               |   2 +-
 .../overview/create-an-experiment.md          |  17 ++-
 .../10-getting-started/overview/index.md      |   4 +
 .../manage-the-feature-flag-lifecycle.md      |   2 +-
 .../overview/send-event-data.md               |   2 +-
 .../static}/boxes-split.txt                   |   0
 .../static}/boxes.htm                         |   0
 .../static}/create-box-split.sh.zip           | Bin
 .../static/split-boxes-chrome.png             | Bin
 .../static/split-boxes-demo.png               | Bin
 .../client-side-sdks/javascript-sdk.md        |  67 ++++++++++-
 .../best-practices/_category_.json            |   6 -
 ...en-targeting-an-account-or-organization.md |  21 ----
 .../best-practices/csv-file-comma.md          |  23 ----
 .../best-practices/o-reilly-book.md           |   9 --
 .../best-practices/split-boxes-demo.md        |  55 ---------
 ...setting-the-order-of-treatments-matters.md |  85 --------------
 .../create-a-feature-flag.md                  |  70 ++++++++++-
 .../default-treatment.md                      |  38 +++---
 ...e-feature-flag-treatments-and-targeting.md |  67 ++++++++++-
 .../dynamic-configurations.md                 |  28 ++++-
 .../faqs/_category_.json                      |   6 -
 ...even-after-deleting-the-targeting-rules.md |  21 ----
 ...-configuration-section-does-not-show-up.md |  16 ---
 .../ensure-a-consistent-user-experience.md    |  34 ------
 ...users-with-a-superseding-targeting-rule.md |  21 ----
 ...can-you-tell-what-treatment-a-user-sees.md |  51 --------
 ...atment-even-if-the-rollout-plan-changes.md |  87 --------------
 ...it-the-number-of-users-in-an-experiment.md |  15 ---
 .../useful-regex-examples-for-targeting.md    |  84 --------------
 ...vs-custom-attributes-in-targeting-rules.md |  19 ---
 ...ween-default-rule-and-default-treatment.md |  26 -----
 .../why-are-some-users-double-bucketed.md     |  70 -----------
 ...e-same-user-id-get-different-treatments.md |  19 ---
 .../maintaining-consistent-treatments.md      |  96 +++++++++++++++
 .../40-feature-management/segments.md         |  27 ++++-
 .../static/create-date-1.png                  | Bin 0 -> 21215 bytes
 .../static/create-date-2.png                  | Bin 0 -> 47682 bytes
 .../static/create-date-3.png                  | Bin 0 -> 69238 bytes
 .../static/create-date-4.png                  | Bin 0 -> 76172 bytes
 .../static/create-date-5.png                  | Bin 0 -> 39969 bytes
 .../static/create-date-6.png                  | Bin 0 -> 16310 bytes
 .../40-feature-management/static/delete-1.png | Bin 0 -> 57372 bytes
 .../40-feature-management/static/delete-2.png | Bin 0 -> 24310 bytes
 .../40-feature-management/static/delete-3.png | Bin 0 -> 45991 bytes
 .../40-feature-management/static/delete-4.png | Bin 0 -> 22807 bytes
 .../static/delete-error.png                   | Bin 0 -> 38689 bytes
 .../does-my-sdk-version-support-semver.png    | Bin
 .../static/dynamic-configuration-section.png  | Bin
 .../40-feature-management/static/regex-1.png  | Bin 0 -> 30795 bytes
 .../40-feature-management/static/regex-2.png  | Bin 0 -> 28886 bytes
 .../40-feature-management/static/regex-3.png  | Bin 0 -> 30262 bytes
 .../40-feature-management/static/regex-4.png  | Bin 0 -> 29160 bytes
 .../40-feature-management/static/regex-5.png  | Bin 0 -> 51487 bytes
 .../40-feature-management/static/rules-1.png  | Bin 0 -> 67686 bytes
 .../40-feature-management/static/rules-2.png  | Bin 0 -> 67258 bytes
 .../static/targeting-1.png                    | Bin 0 -> 30795 bytes
 .../static/targeting-2.png                    | Bin 0 -> 28886 bytes
 .../static/targeting-3.png                    | Bin 0 -> 30262 bytes
 .../static/targeting-4.png                    | Bin 0 -> 29160 bytes
 .../static/targeting-5.png                    | Bin 0 -> 51487 bytes
 .../static/treatment-1.png                    | Bin 0 -> 197798 bytes
 .../static/treatment-2.png                    | Bin 0 -> 119929 bytes
 .../static/use-case-1.png                     | Bin 0 -> 197798 bytes
 .../static/use-case-2.png                     | Bin 0 -> 119929 bytes
 .../target-with-custom-attributes.md          | 109 +++++++++++++++++-
 .../targeting-an-account.md                   |  40 +++++++
 .../viewing-user-treatments.md                |  69 +++++++++++
 .../60-experimentation/setup/index.md         |   8 +-
 72 files changed, 678 insertions(+), 709 deletions(-)
 create mode 100644 docs/feature-management-experimentation/10-getting-started/overview/boxes-demo.md
 rename docs/feature-management-experimentation/{40-feature-management/best-practices/shared => 10-getting-started/static}/boxes-split.txt (100%)
 rename docs/feature-management-experimentation/{40-feature-management/best-practices/shared => 10-getting-started/static}/boxes.htm (100%)
 rename docs/feature-management-experimentation/{40-feature-management/best-practices/shared => 10-getting-started/static}/create-box-split.sh.zip (100%)
 rename docs/feature-management-experimentation/{40-feature-management/best-practices => 10-getting-started}/static/split-boxes-chrome.png (100%)
 rename docs/feature-management-experimentation/{40-feature-management/best-practices => 10-getting-started}/static/split-boxes-demo.png (100%)
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/_category_.json
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/best-practices-when-targeting-an-account-or-organization.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/csv-file-comma.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/o-reilly-book.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/split-boxes-demo.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/best-practices/why-setting-the-order-of-treatments-matters.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/_category_.json
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/dynamic-configuration-section-does-not-show-up.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/ensure-a-consistent-user-experience.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/how-can-you-tell-what-treatment-a-user-sees.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/how-do-i-maintain-the-state-of-a-users-treatment-even-if-the-rollout-plan-changes.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/useful-regex-examples-for-targeting.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/what-are-attributes-in-the-admin-api-vs-custom-attributes-in-targeting-rules.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/why-are-some-users-double-bucketed.md
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/faqs/why-might-the-same-user-id-get-different-treatments.md
 create mode 100644 docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-3.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-4.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-5.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/create-date-6.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/delete-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/delete-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/delete-3.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/delete-4.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/delete-error.png
 rename docs/feature-management-experimentation/40-feature-management/{faqs => }/static/does-my-sdk-version-support-semver.png (100%)
 rename docs/feature-management-experimentation/40-feature-management/{faqs => }/static/dynamic-configuration-section.png (100%)
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/regex-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/regex-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/regex-3.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/regex-4.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/regex-5.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/rules-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/rules-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/targeting-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/targeting-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/targeting-3.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/targeting-4.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/targeting-5.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/treatment-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/treatment-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/use-case-1.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/static/use-case-2.png
 create mode 100644 docs/feature-management-experimentation/40-feature-management/targeting-an-account.md
 create mode 100644 docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md

diff --git a/docs/feature-management-experimentation/10-getting-started/overview/boxes-demo.md b/docs/feature-management-experimentation/10-getting-started/overview/boxes-demo.md
new file mode 100644
index 00000000000..d97122c8df6
--- /dev/null
+++ b/docs/feature-management-experimentation/10-getting-started/overview/boxes-demo.md
@@ -0,0 +1,69 @@
+---
+title: Explore How Feature Flags Affect User Targeting
+sidebar_label: Explore How Feature Flags Affect User Targeting
+description: Learn how to use this visualization tool to help you explore the effects of individual targeting, custom attributes, traffic allocation limits, and dynamic configurations on feature flags.
+sidebar_position: 3
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/best-practices/split-boxes-demo/
+---
+
+## Overview
+
+The Split Boxes demo is a tool to help users understand the interaction between rules and the impact of various features. It’s a simple visualization that allows you to see the impact of individually targeting, custom attribution, limit exposure, and dynamic configuration.
+
+## Using the Boxes Demo
+
+Each box represents a user ID.
+
+![](.././static/split-boxes-demo.png)
+
+* You can individually target using the cell location, such as b8 or j5.
+* You can also create a segment that includes any of the available values.
+* You can create targeting rules using the attributes **row**, **col**, or **account**;
+  
+  * _row_ and _col_ use letters and numbers respectively, usually with "is in list" as the matcher.
+  * Valid account names include: Nike, Apple, LinkedIn, Best Buy, Google, Microsoft, Pinterest, Dell, Slack, Zoom, Samsung, and Disney.
+
+* You can modify the configuration of the treatments by updating any of the values. The `font_size` expects standard HTML sizes such as medium, large, x-large, etc.
+
+## Setting up the Boxes Demo
+
+There are three files attached:
+
+* The HTML contains the SDK and can be run locally or on a server.
+* You need to provide the browser API key for the Split environment where you will update the rollout plan.
+* You also need to provide the feature flag name. These are entered as variables in the HTML:
+
+   ```html
+   <script>
+   var splitAPIKey = "";
+   var splitName = "";
+   </script>
+   ```
+
+   * The Boxes_split.txt file contains an example baseline definition of the feature flag.
+   * The feature flag can be created automatically using the `CreateBoxSplit.sh` script, which uses the Split Admin REST API and the `jq` tool. Run the script with this command line to create the feature flag and add definitions:
+   
+   ```css
+   CreateBoxSplit [Project Name] [Environment Name] [Traffic Type] [Split Name] [Admin API_KEY]
+   ```
+
+   Example:
+
+   ```sql
+   CreateBoxSplit Default Production user front_end_choose_boxes 9enxxxxxxxxxxxxxxxxxxxxxx
+   ```
+
+In Chrome, to see feature flag changes immediately, disable cache in the Network tab of the Developer Tools.
+
+![](.././static/split-boxes-chrome.png)
+
+## Downloads
+
+| File                                                      | Size      | Notes                             |
+| --------------------------------------------------------- | --------- | --------------------------------- |
+| [CreateBoxSplit.sh.zip](.././static/create-box-split.sh.zip) | 1 KB      |                                   |
+| [Boxes\_split.txt](.././static/boxes-split.txt)              | 658 Bytes | *Right-click > Save Link As...* |
+| [Boxes.htm](.././static/boxes.htm)                           | 8 KB      | *Right-click > Save Link As...* |
+
+
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md b/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
index b45467ca0e9..31192672b12 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
@@ -2,7 +2,7 @@
 title: Build a Resilient Integration
 sidebar_label: Build a Resilient Integration
 description: Learn how to build a resilient integration with Harness FME.
-sidebar_position: 7
+sidebar_position: 8
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric-alert-policy.md b/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric-alert-policy.md
index 47493ec22bd..05c471148fb 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric-alert-policy.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric-alert-policy.md
@@ -1,6 +1,6 @@
 ---
 title: Create a Metric Alert Policy
-sidebar_position: 5
+sidebar_position: 6
 ---
 
 import AlertPolicies from '/docs/feature-management-experimentation/shared/alert-policies/index.mdx'
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric.md b/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric.md
index 31d2d554078..ae69d57a466 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-a-metric.md
@@ -1,6 +1,6 @@
 ---
 title: Create a Metric
-sidebar_position: 4
+sidebar_position: 5
 ---
 
 import MetricsSetup from '/docs/feature-management-experimentation/shared/metrics/setup/index.mdx'
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
index 456e85c34c2..84ab0511036 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
@@ -2,7 +2,9 @@
 title: Create an Experiment
 sidebar_label: Create an Experiment
 description: Learn how to create an experiment in Harness FME.
-sidebar_position: 6
+sidebar_position: 7
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment
 ---
 
 ## Overview
@@ -33,6 +35,19 @@ To create an A/B test in Harness FME:
 1. Apply tags to help categorize your experiment (for example, by team, status, or feature area).
 1. Click **Save**. 
 
+### Limiting the number of users in an experiment
+
+You can't directly cap the total number of users who will participate in an experiment. However, you can use the **Limit exposure** option to control the percentage of eligible users who are exposed to the experiment at any given time.
+
+This approach lets you:
+
+- Reduce risk when rolling out changes.
+- Gather results from a smaller sample before expanding to all users.
+
+:::tip
+Start with a lower exposure percentage (for example, 10%) to validate results, then increase it gradually.
+:::
+
 ## View experiment results
 
 Once your experiment is running, Harness FME automatically tracks key metrics and monitors the statistical significance of the results. 
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/index.md b/docs/feature-management-experimentation/10-getting-started/overview/index.md
index 8bd75150d14..3e64a686736 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/index.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/index.md
@@ -2,6 +2,8 @@
 title: Overview
 sidebar_label: Overview
 description: How to make Feature Management & Experimentation work for you
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/best-practices/o-reilly-book
 ---
 
 Harness Feature Management & Experimentation (FME) combines capabilities for feature delivery and control with built-in tools for measurement and learning. FME connects insightful data to every feature release and supports modern practices like continuous delivery and progressive delivery.
@@ -142,6 +144,8 @@ Split has robust data pipelines and attribution logic. If you do find a mismatch
 
 Manage feature flag scheduling, flag sets, and flag lifecycles. Feature flags turn on features to specific users or segments. You can tailor access to beta testers and early adopters based on individual IDs, attributes, dependencies, or percentages. Gradually increase the rollout percentage to limit the blast radius of your releases.
 
+In [this book](https://www.harness.io/resources/feature-flags-best-practices), Split CTO Pato Echague and Pete Hodgson explain how to implement feature-flagged software successfully, and offer some tips to developers on how to configure and manage a growing set of feature flags within your product, maintain them over time, manage infrastructure migrations, and more.
+
 ### Release monitoring
 
 Release monitoring detects the impact of each feature on system performance and user behavior, starting with the earliest stage of a gradual rollout. With detection and triage done at the flag level, you can ship more often and with greater confidence.
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/manage-the-feature-flag-lifecycle.md b/docs/feature-management-experimentation/10-getting-started/overview/manage-the-feature-flag-lifecycle.md
index 67e9f1737ed..58fd036440a 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/manage-the-feature-flag-lifecycle.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/manage-the-feature-flag-lifecycle.md
@@ -2,7 +2,7 @@
 title: Manage the Feature Flag Lifecycle
 sidebar_label: Manage the Feature Flag Lifecycle
 description: Learn how to manage the feature flag lifecycle with Harness FME.
-sidebar_position: 8
+sidebar_position: 9
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/send-event-data.md b/docs/feature-management-experimentation/10-getting-started/overview/send-event-data.md
index 4ccd5cfb0dc..aee934624af 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/send-event-data.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/send-event-data.md
@@ -2,7 +2,7 @@
 title: Send Event Data
 sidebar_label: Send Event Data
 description: Learn how to send event data to Harness FME.
-sidebar_position: 3
+sidebar_position: 4
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/shared/boxes-split.txt b/docs/feature-management-experimentation/10-getting-started/static/boxes-split.txt
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/best-practices/shared/boxes-split.txt
rename to docs/feature-management-experimentation/10-getting-started/static/boxes-split.txt
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/shared/boxes.htm b/docs/feature-management-experimentation/10-getting-started/static/boxes.htm
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/best-practices/shared/boxes.htm
rename to docs/feature-management-experimentation/10-getting-started/static/boxes.htm
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/shared/create-box-split.sh.zip b/docs/feature-management-experimentation/10-getting-started/static/create-box-split.sh.zip
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/best-practices/shared/create-box-split.sh.zip
rename to docs/feature-management-experimentation/10-getting-started/static/create-box-split.sh.zip
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/static/split-boxes-chrome.png b/docs/feature-management-experimentation/10-getting-started/static/split-boxes-chrome.png
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/best-practices/static/split-boxes-chrome.png
rename to docs/feature-management-experimentation/10-getting-started/static/split-boxes-chrome.png
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/static/split-boxes-demo.png b/docs/feature-management-experimentation/10-getting-started/static/split-boxes-demo.png
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/best-practices/static/split-boxes-demo.png
rename to docs/feature-management-experimentation/10-getting-started/static/split-boxes-demo.png
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
index 1c11d340c3d..73868d49bdb 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
@@ -1,6 +1,8 @@
 ---
 title: JavaScript SDK
 sidebar_label: JavaScript SDK
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/why-are-some-users-double-bucketed/
 ---
 
 import Tabs from '@theme/Tabs';
@@ -1350,4 +1352,67 @@ ReactGA.pageview(window.location.pathname);
 The following example applications detail how to configure and instantiate the JavaScript SDK on commonly used platforms:
 
 * [Basic HTML](https://github.com/splitio/example-javascript-client)
-* [AngularJS](https://github.com/splitio/angularjs-sdk-examples)
\ No newline at end of file
+* [AngularJS](https://github.com/splitio/angularjs-sdk-examples)
+
+## Troubleshooting
+
+### User IDs Being Double Bucketed (SDK_READY_TIMED_OUT Event Issue)
+
+Using the JavaScript SDK, some percentage of User IDs are double bucketed: the same User ID is processed in the Control block, and at a later call in an actual treatment block.
+
+One possible root cause is that the JavaScript SDK engine completes fetching treatments after the `requestTimeoutBeforeReady` or `readyTimeout` parameter expires, firing the browser event `SDK_READY_TIMED_OUT`.
+
+This tends to happen for mobile users with slow internet connections.
+
+If the JavaScript code does not properly handle this event—for example, if it only raises an error and exits:
+
+```javascript
+var client = SdkFactory.client();
+this.clientReady = new Promise(function(resolve, reject) {
+  client.once(client.Event.SDK_READY, function() {
+    resolve();
+  });
+  client.once(client.Event.SDK_READY_TIMED_OUT, function() {
+    reject(new Error("Client SDK Ready Timed Out"));
+  });
+});
+```
+
+There are two possible events:
+
+* If the `SDK_READY` event comes first, the promise resolves, and everything works as expected.
+* If the `SDK_READY_TIMED_OUT` event comes first, the promise rejects and remains rejected, meaning that any `.catch()` attached will always be called—even if the SDK eventually becomes ready later.
+
+If you add a `.catch()` that does not return or throw an error, the wrapper code might continue executing as if the SDK is ready, even when it is not. For example:
+
+```javascript
+this.clientReady.catch(() => {}).then(() => {
+});
+```
+
+Here, the `.catch()` swallows the error without returning or throwing, causing the promise chain to appear "recovered." This can cause code to run prematurely against an SDK that isn’t ready.
+
+Even if the `SDK_READY_TIMED_OUT` event fires, the SDK might become ready a few milliseconds later and emit the `SDK_READY` event. You should handle these events only when you actually want to respond to them.
+
+1. Allow the SDK to retry fetching treatments a couple of times before giving up, increasing the chance the SDK becomes ready within the timeout:
+
+   ```javascript
+   startup: {
+   requestTimeoutBeforeReady: 5,    // 5 seconds
+   readyTimeout: 5,                 // 5 seconds
+   retriesOnFailureBeforeReady: 2,  // 2 retries
+   }
+   ```
+
+1. Configure the SDK to store `Splits` and `mySegments` data in the browser’s `LOCALSTORAGE`. This caches data between page loads and reduces fetch time on subsequent page visits, making the SDK ready faster:
+
+   ```javascript
+   storage: {
+   type: 'LOCALSTORAGE',
+   prefix: 'MYPREFIX'
+   },
+   ```
+
+   Use a custom prefix to prevent data collision across projects.
+
+For more information, see the [API reference documentation](https://docs.split.io/docs/javascript-sdk-overview#section-configuration).
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/_category_.json b/docs/feature-management-experimentation/40-feature-management/best-practices/_category_.json
deleted file mode 100644
index 5dc379bfa40..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/_category_.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-    "label": "Best practices",
-    "collapsible": "true",
-    "collapsed": "true",
-    "className": "red"
-}
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/best-practices-when-targeting-an-account-or-organization.md b/docs/feature-management-experimentation/40-feature-management/best-practices/best-practices-when-targeting-an-account-or-organization.md
deleted file mode 100644
index e7dcf5c92f6..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/best-practices-when-targeting-an-account-or-organization.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Best practices when targeting an account or organization
-sidebar_label: Best practices when targeting an account or organization
-sidebar_position: 4
----
-
-import Link from "@docusaurus/Link";
-
-Traffic types that target using a key for accounts or organizations (or schools or hospitals or...) as opposed to individual users are most often based on managing entitlements or for B2B applications.  Of course, you can also target based on a department, plant, location or any level of granularity for which you have an associated key.  We'll use 'account' as a proxy for all of the above.
-
-## Consistency of Experience
-
-When you want consistency of experience for all users within a single account, then use the account traffic type. This ensures that by providing a single ID - the account ID - you get the benefit of consistency without having to provide any complex data objects around users.
-
-If you want to measure the impact of a feature being rolled out by accounts through experimentation, avoid using SUM or COUNT metrics. As an example, say account A has 1,000 users but account B has two users. A metric that COUNTs or SUMs activity across these two accounts will be skewed in favor of the bigger account. Conversely, if you create metrics that measure AVERAGEs or PERCENTAGEs, data is normalized across accounts.
-
-As a B2B company, if you are OK with rolling out some of the features without the guarantee of consistency of experience for all users within a single account, then use a user traffic type. This also opens up a world of experimentation for you as you can measure SUM, COUNTs, AVERAGEs, PERCENTAGEs.
-
-Another option is to roll out a feature flag by user, but use company as a custom attribute to ensure everyone in specific (or all) companies get the same experience.  This allows you to create a consistent experience within each organization, but still evaluate the results on a user by user basis.  Of course, as noted above, you will not be able to control the distribution if some companies have far more users than others.  More on that in [this article](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience).
-
-These are tradeoffs that individual teams can make. You can roll out some features by accounts and others by users and Split allows you manage the experiences for each.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/csv-file-comma.md b/docs/feature-management-experimentation/40-feature-management/best-practices/csv-file-comma.md
deleted file mode 100644
index ad72fede326..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/csv-file-comma.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: How to upload CSV file that contains IDs with comma character to new segment?
-sidebar_label: How to upload CSV file that contains IDs with comma character to new segment?
-sidebar_position: 1
----
-
-### Question
-
-Using the CSV upload feature in segment page in Split user interface, is it possible to upload user IDs in the CSV file that contain comma? The standard escape for CSV comma values is encapsulating the column value with double quotes like the example below:
-```
-"user1, key1"
-"user2, key2"
-"user3, key3"
-```
-
-### Answer
-
-The CSV upload functionality in Split user interface automatically considers each line as a unique user  id, so there is no need to add the double quotes, as they are added to the final user IDs that are imported to a Split segment. To use the example above, remove the double quotes, as below:
-```
-user1, key1
-user2, key2
-user3, key3
-```
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/o-reilly-book.md b/docs/feature-management-experimentation/40-feature-management/best-practices/o-reilly-book.md
deleted file mode 100644
index 4f996eef7b5..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/o-reilly-book.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: "O'Reilly book: Feature Flag Best Practices"
-sidebar_label: "O'Reilly book: Feature Flag Best Practices"
-sidebar_position: 2
----
-
-import Link from "@docusaurus/Link";
-
-In [this book](https://try.split.io/hubfs/pdfs/oreilly-feature-flag-best-practices/OReilly_and_Split_Feature_Flag_Best_Practices.pdf), Split CTO Pato Echague and Pete Hodgson explain how to implement feature-flagged software successfully, and offer some tips to developers on how to configure and manage a growing set of feature flags within your product, maintain them over time, manage infrastructure migrations, and more.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/split-boxes-demo.md b/docs/feature-management-experimentation/40-feature-management/best-practices/split-boxes-demo.md
deleted file mode 100644
index fa90e41153c..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/split-boxes-demo.md
+++ /dev/null
@@ -1,55 +0,0 @@
----
-title: Split Boxes Demo
-sidebar_label: Split Boxes Demo
-helpdocs_is_private: false
-helpdocs_is_published: true
-sidebar_position: 5
----
-
-import Link from "@docusaurus/Link";
-
-The Split Boxes demo is a tool to help users understand the interaction between rules and the impact of various features. It's a simple visualization that allows you to see the impact of individually targeting, custom attribution, limit exposure, and dynamic configuration.
-
-## Using the Boxes Demo
-
-Each box represents a user ID.  
-
-![](./static/split-boxes-demo.png)
-
-You can individually target using the cell location, such as b8 or j5.  You can also create a segment that includes any of the available values.
-
-You can create targeting rules using the attributes row, col, or account; row and col will use letters and numbers respectively, usually with 'is in list' as the matcher.  Valid account names include Nike, Apple, LinkedIn, Best Buy, Google, Microsoft, Pinterest, Dell, Slack, Zoom, Samsung and Disney.
-
-You can modify the configuration of the treatments by updating any of the values.  The font_size expects standard HTML sizes, such as medium, large, x-large, etc.
-
-## Setting up the Boxes Demo
-
-There are three files attached.  The HTML contains the SDK and can be run locally or on a server.  You need to provide the browser API key for whichever Split environment in which you will be updating the rollout plan.  You also need to provide the name of the feature flag.  These are entered as vars in the HTML.
-
-```html
-<script>
-var splitAPIKey=""
-var splitName=""
-```
-
-The Boxes_split.txt file contains an example baseline definition of the feature flag.
-
-The feature flag can be created automatically using CreateBoxSplit.sh script which uses Split Admin Rest API, and jq tool. Run the script with the following command line to create the feature flag and add the definitions:
-
-CreateBoxSplit [Project Name] [Environment Name] [
-Traffic Type] [Split Name] [Admin API_KEY]
-
-For example
-```
-CreateBoxSplit Default Production user front_end_choose_boxes 9enxxxxxxxxxxxxxxxxxxxxxx
-```
-
-In Chrome, to see feature flag changes immediately you should Disable cache for the Network in the browser's Developer Tools.
-
-![](./static/split-boxes-chrome.png)
-
-<br />
-
-[CreateBoxSplit.sh.zip](./shared/create-box-split.sh.zip) &emsp; | 1 KB<br />
-[Boxes_split.txt](./shared/boxes-split.txt) <font color='grey'> _(right-click > **Save Link As...**)_ </font> &emsp; | 658 Bytes<br />
-[Boxes.htm](./shared/boxes.htm) <font color='grey'> _(right-click > **Save Link As...**)_ </font> &emsp; | 8 KB<br />
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/best-practices/why-setting-the-order-of-treatments-matters.md b/docs/feature-management-experimentation/40-feature-management/best-practices/why-setting-the-order-of-treatments-matters.md
deleted file mode 100644
index 0114ebc6192..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/best-practices/why-setting-the-order-of-treatments-matters.md
+++ /dev/null
@@ -1,85 +0,0 @@
----
-title: Why setting the order of treatments matters
-sidebar_label: Why setting the order of treatments matters
-sidebar_position: 3
----
-
-With Split, rules are evaluated from the top down. 
-
-1. Individually target - users you explicitly assign to a treatment
-2. Limit exposure - If set to less than 100%, the excluded users are given the default treatment you specify in the treatments list.
-3. Targeting rules - specific subsets of users targeted by attributes
-4. The final rule, which is labeled as "Serve" or "Distribute treatments as follows" in the UI - users not individually targeted and not targeted by a targeting rule. This is a "catch-all" which assigns treatments for the remaining population not previously excluded or targeted.
-
-As with rules, the order of treatments matters.  For example, if you have 3 (or more) treatments you’ll want to think through order based on how you want things to change as you change percentages, particularly if you want to maintain a consistent user experience, as described [here](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience).
-
-Let's use changing the default rule as an example, with `status_quo`, `treatment1`, `treatment2` set to 33/33/34.  You want to move everyone out of 'status_quo' and set `treatment2` to 67%.
-
-If you had your treatments in the above order (`status_quo`, `treatment1`, `treatment2`), setting `status_quo` to 0% and `treatment2` to 67% would cause the users from `status_quo` to move to `treatment1` and the users from `treatment1` to move to `treatment2`.  Clearly, this may not be the right experience for your users.
-
-If, however, you have `status_quo` in the middle of the order (`treatment1`, `status_quo`, `treatment2`), then when you set `status_quo` to 0% and `treatment2` to 67% this does not impact the users in `treatment1`.  This would also be true if you set `treatment1` to 66% and `status_quo` to 0%.  The 34% originally assigned to `treatment2` would remain consistent.  See how this can apply to rolling out an experiment below.
-
-Let's say you have more than three treatments.  In this case, the `status_quo` was the safe treatment, and if any of the treatments were found to have a bad experience the plan is to move those users to `status_quo`.  If we decide `treatment2` was bad and we try to give that 20% to `status_quo` by setting `treatment2` to zero and increasing `status_quo` to 40%, we will actually cause an undesired shift: users in `treatment1` would get `status_quo` as well since the first 40 buckets, which had been distributed between `status_quo` and `treatment1`, now all get `status_quo`. Buckets 40-59, which had been getting `treatment2` would then get `treatment1`. Users who were getting `treatment3` and `treatment4` are unaffected.
-
-<table style={{width: '100%', height: '332px'}}>
-  <thead>
-    <tr style={{height: '22px'}}>
-      <th style={{textAlign: 'center', height: '22px'}}>Even Distribution</th>
-      <th style={{textAlign: 'center', height: '22px'}}>Buckets</th>
-      <th style={{textAlign: 'center', height: '22px'}}>T2 to status_quo</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr style={{height: '22px'}}>
-      <td style={{textAlign: 'center', height: '22px'}}>status_quo</td>
-      <td style={{textAlign: 'center', height: '22px'}}>0-19</td>
-      <td style={{textAlign: 'center', height: '44px'}} rowspan="2">status_quo</td>
-    </tr>
-    <tr style={{height: '22px'}}>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment1</td>
-      <td style={{textAlign: 'center', height: '22px'}}>20-39</td>
-    </tr>
-    <tr style={{height: '22px'}}>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment2</td>
-      <td style={{textAlign: 'center', height: '22px'}}>40-59</td>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment1</td>
-    </tr>
-    <tr style={{height: '22px'}}>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment3</td>
-      <td style={{textAlign: 'center', height: '22px'}}>60-79</td>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment3</td>
-    </tr>
-    <tr style={{height: '22px'}}>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment4</td>
-      <td style={{textAlign: 'center', height: '22px'}}>80-99</td>
-      <td style={{textAlign: 'center', height: '22px'}}>treatment4</td>
-    </tr>
-  </tbody>
-</table>
-
-One way to 'move' users between multivariate treatments while only removing users from `treatment2` is to take advantage of [Dynamic Configuration](/docs/feature-management-experimentation/feature-management/dynamic-configurations), assuming you can represent the differences between treatments using a set of configured values.  In this case, you'd set the configuration of `treatment2` to the `status_quo` settings, which would give them the `status_quo` experience.
-
-| Even Distribution |	Buckets |	Dynamic Config |
-| --- | --- | --- |
-| status_quo | 0-19 | status_quo |
-| treatment1 | 20-39 | treatment1 |
-| treatment2 | 40-59 | status_quo |
-| treatment3 | 60-79 | treatment3 |
-| treatment4 | 80-99 | treatment4 |
- 
-
-## Limiting traffic
-
-Limit exposure allows you to exclude a percentage of users from your rules. For example, you can set exposure to 20% and then set the "Distribute treatments as follows" setting to 0/50/50 for `status_quo`, `treatment1`, and `treatment2`.  Effectively, each of the two 50/50 treatments gets 10% of traffic.  The advantage of this approach is that as you add traffic to the rules by increasing the limit exposure setting, no users switch from, in our example, `treatment1` to `treatment2`.  They move from `status_quo` (set as the default treatment) to one of the two treatments.
-
-This is also useful if you have a number of complex rules and you want to increase the participation across all the rules at once. 
-
-## Including traffic for experimentation
-
-But what if you want to see all of the impressions on the Metrics impact tab? Even if you're not going to include the unallocated `status_quo` users in the analysis. Again, order matters if you want to make sure adding traffic doesn't move users that are in the experiment between treatments.  In this case, you'd order the excluded users to be between the two treatments.
-
-As you roll-out more users, by decreasing the percentage in `status_quo` and increasing each of the other treatments, existing users in those two treatments will remain in the same treatment they were in previously.
-
-## Individually target
-
-Individually targeted evaluations are done in the order of the treatments. Consider a situation where user 12345 is individually targeted for `status_quo`. Even if user 12345 is in the beta-users or Employee segments individually targeted for `treatment1`, they will still get `status_quo`, because that's evaluated first.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md b/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md
index af097027f59..3497a3c1d22 100644
--- a/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md
@@ -3,6 +3,9 @@ title: Feature Management in Harness FME
 sidebar_label: Overview
 description: Learn about feature management in Harness FME.
 sidebar_position: 1
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules
+  - /docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience
 ---
 
 ## Overview
@@ -56,7 +59,60 @@ To create a feature flag, do the following:
 
 1. To configure your feature flag for a particular environment, select the environment on the left panel, and click the Initiative environment button within the Definition tab. 
 
-## Next step
+## Ensuring a consistent user experience
+
+### Random and Deterministic Treatment Assignment
+
+Harness FME (powered by Split) uses a **deterministic hashing algorithm** to decide which treatment (experience) to deliver for a feature flag. The algorithm takes two main inputs:
+
+- **User ID:** This can be an anonymous user ID, logged-in user ID, account number, or any string representing the entity. The ID’s type is specified by its [traffic type](/docs/feature-management-experimentation/management-and-administration/fme-settings/traffic-types/).
+- **Feature flag:** When the SDK initializes, it downloads targeting plans for all flags. Each flag has a unique **seed** — a random number cached as part of the rollout plan and consistent across all SDKs evaluating that flag.
+
+### How Bucketing Works
+
+For example, when `user_1234` calls `getTreatment` on a flag with seed `123456`:
+
+1. The user ID and flag seed are combined and processed through the deterministic hashing algorithm.
+2. The resulting hash is converted to a number and normalized using `Mod 100` into one of 100 buckets.
+3. The user consistently falls into the **same bucket** every time for that feature flag, regardless of which SDK evaluates it.
+
+This means the bucket, not the user, is assigned the treatment.
+
+- If a feature is exposed to 10% of users, users in buckets 0-9 get the feature; others do not.
+- If exposure increases to 30%, users in buckets 0-29 get the feature.
+
+### Independence and Environment-Specific Seeds
+
+- Split **does not retain any prior knowledge** about users.
+- The evaluation happens **every time** a treatment is requested.
+- Each feature flag seed is unique per environment (e.g., staging vs production), so bucketing differs across environments.
+
+### Limit Exposure and Bucketing
+
+Limit exposure controls participation in targeting rules:
+
+- All users are first bucketed by **limit exposure**.
+- For example, with a 50/50 exposure setting:
+  - Buckets 0-49 are subject to targeting and default rules.
+  - Buckets 50-99 receive the default treatment directly, excluding them from the experiment or feature.
+
+### Treatment Order Matters
+
+The order in which treatments are defined can affect the consistency of user experience. [Learn more about treatment order](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#treatment-ordering-and-traffic-distribution).
+
+### Traffic Type and Targeting Granularity
+
+- **User-level targeting:** Use UUIDs (e.g., logged-in user IDs) for consistent user experiences.
+- **Anonymous users:** Use cookies, session IDs, or device IDs, but note that users might not receive consistent experiences if switching devices or clearing cookies.
+- **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/targeting-an-account).
+
+### Important Consideration for Account Targeting
+
+If you do a 50/50 split by account, large accounts with many users might unevenly skew the user count on one side of the experiment, potentially leading to an actual end-user distribution higher than 50%.
+
+Harness FME ensures a consistent user experience by combining deterministic hashing with seed-based bucketing and configurable limit exposure, enabling reliable and predictable feature rollouts across different users and environments.
+
+## Create a rollout plan
 
 After you have created your feature flag, you can [define treatments and set targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) for your feature flag. This is also known as creating a rollout plan.
 
@@ -94,6 +150,18 @@ To avoid this issue, your workspace administrator might consider one of the foll
   * Users request a peer to review and approve changes before publishing.
   * Admins assign a group of authorized approvers (often the same users with default edit rights).
 
+### Cannot delete feature flag from UI even after deleting the targeting rules
+
+When trying to delete a feature flag that exists in multiple Environments, an error message appears preventing deletion—even after deleting all the custom targeting rules.
+
+![](./static/delete-error.png)
+
+The error refers to all feature flag definitions within each environment, including Treatments, Individual targets, Dynamic Configurations, Default Rules, and Custom Targeting Rules.
+
+1. In each environment where the flag is present, click the second (...) icon near the top right corner and select **Delete targeting rules**.
+1. Repeat this step for **all environments** where the feature flag exists.
+1. After all targeting rules and definitions are removed from every environment, you should be able to successfully delete the feature flag from the UI.
+
 ### Can feature flags move between workspaces?
 
 There is no supported way to migrate existing feature flags from one workspace to another. This limitation exists because environments, API keys, segments, and traffic types are all scoped to a single workspace. 
diff --git a/docs/feature-management-experimentation/40-feature-management/default-treatment.md b/docs/feature-management-experimentation/40-feature-management/default-treatment.md
index 35ad0da2539..e0f7cdaadee 100644
--- a/docs/feature-management-experimentation/40-feature-management/default-treatment.md
+++ b/docs/feature-management-experimentation/40-feature-management/default-treatment.md
@@ -1,37 +1,35 @@
 ---
 title: Default treatment
 sidebar_label: Default treatment
-helpdocs_is_private: false
-helpdocs_is_published: true
-description: ""
+description: "Learn about the default treatment in Harness FME."
 sidebar_position: 14
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment
 ---
 
+## Overview
+
 The default treatment is returned by the SDK in the following scenarios:
 
 * **The end user does not meet any defined conditions:** The default treatment is shown to end users who do not meet any of the conditions in the targeting rules.
 
 * **The flag is killed:** If a particular feature flag is killed, the default treatment overrides the existing targeting rules and is returned for **all** customers.
 
-<!-- can we replace the following content : -->
-
-:::tip[Tips]
-You can set any of the treatments in your targeting rules as the default. We recommend choosing the safest treatment for your customers as the default treatment (that is, _off_, _old version_) when beginning the rollout. However, when you complete the rollout, you may want to make the new experience the default in the case that feature is accidentally killed before being removed.
+:::tip
+You can set any of the treatments in your targeting rules as the default. Harness recommends choosing the safest treatment for your customers as the default treatment (that is, _off_, _old version_) when beginning the rollout. However, when you complete the rollout, you may want to make the new experience the default in the case that feature is accidentally killed before being removed.
 :::
 
-<!-- with this :
+## How default treatments differ from default rule
 
-Therefore, if a feature flag is killed or the customer is not exposed to any targeting rules, the flag serves a treatment selected by you. This is also known as the default treatment.
+It’s important to understand the difference between the default treatment and the default rule, especially when using traffic allocation:
 
-:::tip[Tip]
-While you can set any of the treatments in your targeting rules as the default,
-your default treatment should always be one that exposes fully tested and safe code.
-* In an **on/off** feature flag, the default treatment is typically set to **'off'**.
-* In a multivariant feature flag, the default might be **'off'**, or it might be defined as the treatment that is currently used by 100% of traffic.
-* At the start of a percentage rollout, we recommend choosing the safest treatment for your customers as the default treatment (that is, **'off'** or **'old version'**).
-* At the end of a percentage rollout (at 100% rolled out), you may want to make the new experience the default in case the feature is accidentally killed before being removed.
-
-To set this treatment, refer to the [Target customers](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) guide.
-:::
+* When you call the SDK’s `getTreatment`, it is called for 100% of users who encounter the feature flag. Every user will receive some treatment, whether on, off, or something else.
+* Traffic Allocation limits the percentage of users who are subject to targeting rules and the default rule. For example, if you set traffic allocation to 10%, then only 10% of users are evaluated against your rules. The remaining 90% will receive the default treatment.
+* For scenarios with multiple treatments, such as `version1`, `version2`, and `unallocated` (which means no feature exposure), you might:
+  
+  * Set Traffic Allocation to 10%, exposing 10% of users to targeting rules.
+  * Configure the Default Rule to split that 10% between `version1` and `version2`.
+  * Set the Default Treatment to `unallocated`, which 90% of users will get.
 
--->
\ No newline at end of file
+* Traffic Allocation helps control user exposure during rollouts and experiments, allowing gradual inclusion without impacting users outside the allocated percentage.
+* In simple two-treatment cases, traffic allocation and default treatment can sometimes produce equivalent outcomes.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md b/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md
index d65586d4adf..9ec3944318f 100644
--- a/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md
+++ b/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md
@@ -3,11 +3,14 @@ title: Define feature flag treatments and targeting
 sidebar_label: Define feature flag treatments and targeting
 description: ""
 sidebar_position: 3
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/best-practices/why-setting-the-order-of-treatments-matters
+  - /docs/feature-management-experimentation/feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule
 ---
 
 ## Define feature flag treatments and targeting
 
-Afer you [create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag), you can define treatments, dynamic configurations, and targeting rules. 
+After you [create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag), you can define treatments, dynamic configurations, and targeting rules. 
 
 This allows you to deploy features for internal testing and beta releases. You can also progressively roll out features to your production environment.
 
@@ -104,6 +107,24 @@ In the Targeting rules area, you can:
 If you have at least one targeting rule with ___percentage distribution___, then you can ___compare metric results between feature flag treatments___ (on the [Metrics impact tab](/docs/feature-management-experimentation/experimentation/experiment-results/viewing-experiment-results/).
 :::
 
+### Overriding individual targets with rules
+
+Targeting rules are evaluated from top to bottom, meaning that the higher a rule appears, the higher its priority. You can override individually targeted users by placing a more specific rule above the one that includes them.
+
+![](./static/rules-1.png)
+
+For example, if you have a rule that individually targets users by ID or segment, but you need to give certain users a different treatment based on an attribute (such as slow network speed), create a higher-priority rule for that attribute.
+
+In the **Set targeting rules** section:
+
+1. Add a rule that matches the overriding attribute (for example, _network speed ≤ 3G_).
+1. Specify the desired treatment (e.g., _Off_).
+1. In the **Else if** part of the rule, include your original segment or user IDs.
+
+![](./static/rules-2.png)
+
+This setup ensures that the overriding condition is applied before the individual targeting rule.
+
 ## Timed releases for feature flags
 
 You can implement a feature toggle that switches between `on` and `off` automatically at a certain time using one of the following methods:
@@ -151,3 +172,47 @@ Feature flag rules are evaluated in the following order:
 Individual targets are evaluated in order. For example, if **Bob** is a user ID in the **Internal_QA** segment, then **Bob** will get **on** even though you’ve specifically assigned that key **off**.
 
 ![](./static/feature-flag-rules-evaluation-order-example.png)
+
+### Treatment ordering and traffic distribution
+
+With Split, the order of treatments within the rules matters.
+
+Rules are evaluated top-down as described above, but within each rule, treatment order determines how traffic is distributed among treatments. For example, if you have three treatments — `status_quo`, `treatment1`, and `treatment2` — evenly split at roughly 33% each, and you want to move everyone out of `status_quo` and assign 67% to `treatment2`, the order of treatments affects user experience.
+
+If the order is `status_quo`, `treatment1`, `treatment2`, setting `status_quo` to 0% and `treatment2` to 67% will cause users to shift from `status_quo` to `treatment1`, and from `treatment1` to `treatment2`. This may not be the intended experience.
+
+If the order is `treatment1`, `status_quo`, `treatment2`, then adjusting percentages will move users from `status_quo` to `treatment2` only, keeping `treatment1` users unaffected.
+
+This is especially important when you have more than three treatments. For instance, if `status_quo` is your safe fallback and you want to move users from a problematic `treatment2` to `status_quo`, simply changing percentages can cause users in treatment1 to unexpectedly shift to `status_quo`.
+
+| Even Distribution | Buckets | After Moving T2 to status\_quo |
+| ----------------- | ------- | ------------------------------ |
+| status\_quo       | 0-19    | status\_quo                    |
+| treatment1        | 20-39   | status\_quo                    |
+| treatment2        | 40-59   | treatment1                     |
+| treatment3        | 60-79   | treatment3                     |
+| treatment4        | 80-99   | treatment4                     |
+
+One way to avoid bucket shifts while moving users between treatments is to use [Dynamic Configuration](/docs/feature-management-experimentation/feature-management/dynamic-configurations/). You keep bucket assignments the same, but change the configuration values of treatments.
+
+| Even Distribution | Buckets | Dynamic Configuration |
+| ----------------- | ------- | --------------------- |
+| status\_quo       | 0-19    | status\_quo           |
+| treatment1        | 20-39   | treatment1            |
+| treatment2        | 40-59   | status\_quo           |
+| treatment3        | 60-79   | treatment3            |
+| treatment4        | 80-99   | treatment4            |
+
+### Limiting traffic exposure
+
+Limit exposure allows you to exclude a percentage of users from your targeting rules. For example, setting exposure to 20% and distributing treatments as 0/50/50 for `status_quo`, `treatment1`, and `treatment2` means each treatment gets 10% of total traffic. Increasing exposure moves new users from the default `status_quo` to treatments without reshuffling users between treatments.
+
+This helps when you want to ramp participation across multiple complex rules simultaneously.
+
+### Including excluded traffic for experimentation
+
+If you want to see all impressions in the Metrics impact tab, including those from users in the excluded default (`status_quo`) treatment, order treatments so excluded users fall between treatments. This way, adding traffic won’t cause users to switch treatments unexpectedly.
+
+### Individual targets override all
+
+Individually targeted users always receive their assigned treatment first. For example, if user `12345` is individually targeted for `status_quo`, they will get that treatment even if they belong to a segment targeted for `treatment1`.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md b/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md
index 7638698b2d9..a1778f87f55 100644
--- a/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md
+++ b/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md
@@ -1,11 +1,15 @@
 ---
 title: Dynamic configuration
 sidebar_label: Dynamic configuration
-description: ""
+description: "Learn how to use Dynamic Configuration in feature flags, enabling you to deliver flexible, fine-tuned experiences by configuring flag treatments with customizable parameters. Learn how to set up, manage, and troubleshoot dynamic configurations to optimize your feature rollouts."
 sidebar_position: 5
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/dynamic-configuration-section-does-not-show-up/
 ---
 
-You can attach configurations to any of your treatments on a feature flag's Definition tab to dynamically control components of your features, such as:
+## Overview
+
+You can attach configurations to any of your treatments on a feature flag's **Definition** tab to dynamically control components of your features, such as:
 
 * Color, size and other visual components of your application
 * The copy or text in different areas of your application
@@ -24,7 +28,7 @@ These configurations give you the ability to make modifications to your features
 To set up dynamic configuration, do the following:
 
 1. From the left navigation, select **Feature flags**, then select a feature flag. The Definition tab appears.
-2. In the Dynamic configuration area, select either the Key-value pairs or JSON format and fill out the following fields:
+2. In the **Dynamic configuration** area, select either the Key-value pairs or JSON format and fill out the following fields:
 
   * Key-value pairs
   * JSON
@@ -79,4 +83,20 @@ Additionally, you can switch between key-value and JSON editing when configuring
 
 ## Configuration size limit
 
-To make sure configurations are not too large, the configurations for each feature flag are limited at 1KB in size. If you need a larger set of configurations, contact support@split.io
\ No newline at end of file
+To make sure configurations are not too large, the configurations for each feature flag are limited at 1KB in size. If you need a larger set of configurations, contact support@split.io.
+
+## Troubleshooting
+
+### Dynamic configuration section does not show up
+
+When clicking on any feature flag, the Dynamic configuration section does not show up, even though other users see the section from their browser.
+
+This issue is typically caused by the Split UI loading an outdated cached version of the page from before dynamic configuration was available.
+
+Clear your browser cache to force the UI to load the latest content. To confirm, open an Incognito window and log in to `app.split.io`. If the dynamic config section appears there, clearing your cache will fix the issue.
+
+For example, in Google Chrome:
+
+1. Go to **Settings** → **Privacy and security** → **Clear browsing data**.
+1. Select **Cookies and other site data**.
+1. Click **Clear data**.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/_category_.json b/docs/feature-management-experimentation/40-feature-management/faqs/_category_.json
deleted file mode 100644
index 1ec77f6e720..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/_category_.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-    "label": "FAQs",
-    "collapsible": "true",
-    "collapsed": "true",
-    "className": "red"
-}
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules.md b/docs/feature-management-experimentation/40-feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules.md
deleted file mode 100644
index 9e362d79d00..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Cannot delete feature flag from UI even after deleting the targeting rules
-sidebar_label: Cannot delete feature flag from UI even after deleting the targeting rules
-sidebar_position: 7
----
-
-### Issue
-
-When trying to delete a feature flag that exist in multiple Environments, The error below show up:
-
-![](https://help.split.io/hc/article_attachments/15734682207117)
-
-However, after deleting all the custom targeting rules, the error persists.
-
-### Answer
-
-The error is referring to all the feature flag definitions within the environment, meaning: Treatments, Individual targets, Dynamic Configs and Default Rules, in addition to the Custom Targeting Rules.
-
-To delete all the definitions, click on the second (...) icon from the top right and click **Delete targeting rules**.
-
-Repeat the same action for all other Environments that the feature flag is added to. Then the delete feature flag option will work successfully.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/dynamic-configuration-section-does-not-show-up.md b/docs/feature-management-experimentation/40-feature-management/faqs/dynamic-configuration-section-does-not-show-up.md
deleted file mode 100644
index aaf3cb80067..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/dynamic-configuration-section-does-not-show-up.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Dynamic configuration section does not show up
-sidebar_label: Dynamic configuration section does not show up
-sidebar_position: 2
----
-
-### Issue:
-
-When clicking on any feature flag, the Dynamic configuration section does not show up, even though other users see the section from their browser.
-
-### Answer:
-This issue is typically caused by the Split user interface using an old page content when Split did not have the dynamic configuration feature, clearing the cache usually fix the issue. 
-
-To confirm the root cause, open an Incognito browser window and access app.split.io. If the dynamic config section show up, then make sure to clear the browser cache. for example in Chrome, goto Chrome Settings->Clear Browsing Data, then check **Cookies and other site data** box, then click clear.
-
-![](./static/dynamic-configuration-section.png)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/ensure-a-consistent-user-experience.md b/docs/feature-management-experimentation/40-feature-management/faqs/ensure-a-consistent-user-experience.md
deleted file mode 100644
index 73b3f77d4da..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/ensure-a-consistent-user-experience.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: How does Harness FME ensure a consistent user experience?
-sidebar_label: How does Harness FME ensure a consistent user experience?
-sidebar_position: 9
----
-
-## Random and Deterministic
-
-Split SDKs use a deterministic hashing algorithm to decide what experience (treatment) to deliver for a feature flag. The algorithm takes two inputs:
-
-* The user ID. The ID can be an anonymous user ID, a logged in user ID, an account number or any other entity representable as a string. The type of the ID is indicated by its [traffic type](/docs/feature-management-experimentation/management-and-administration/fme-settings/traffic-types/). 
-* The feature flag. When the SDK initializes, it downloads targeting plans for all flags. The seed, which is a random number for each feature flag, is cached as part of the rollout plan, and is consistent for every SDK that evaluates that flag.
-
-For example, user_1234 hits a getTreatment and the flag has the seed 123456.  These are combined and run through a deterministic hashing algorithm, which is consistent every time.  That hash value is converted into a number, which is then normalized using Mod 100 and put into one of 100 buckets.  The result is the user is put into the same bucket every time, for that feature flag, independent of which SDK calls the flag.  One way to think of it is the bucket is what’s assigned a treatment, not the user.
-
-As long as the user has the same ID, they get the same hash value, the seed will be the same, and the resulting Mod 100 value and bucket are consistent. If a feature is exposed to 10% of the users and the bucket for a hashed value is 26, that user is not assigned the feature. If the exposure rises to 30%, then the user gets the feature.
-
-Split doesn't know anything about user ahead of time. The evaluation described above happens every time for every user and the user is put in the same bucket. The seed for a feature flag is unique to the flag and the environment. If a customer has a staging and a production environment, the seed will have a unique value for each. 
-
-## Limit exposure
-
-Limit exposure can play a role in this bucketing as well.  All users are bucketed first based on whether or not they are participating in the targeting rules.  Users are first bucketed by limit exposure and then bucketed by the rules. 
-
-If you set your exposure to 50/50 then users put in buckets 0-49 will be subject to the targeting rules and the default rule, at which time they will be bucketed again.  Users in buckets 50-99 will be given the default treatment, which is the experience you give to users that you don't want to include in the new feature or experiment.
-
-## Order of Treatment
-
-The order in which you set your treatments can also have an impact on ensuring a consistent experience.  This is described in more detail [here](/docs/feature-management-experimentation/feature-management/best-practices/why-setting-the-order-of-treatments-matters).
-
-## Traffic Type
-
-As noted above, you can set the granularity of your targets using traffic type.  You can target users using a UUID, usually something that is created by them logging into your site.  If the user is anonymous, you can use a cookie or session ID or device ID.  But in that case you have a chance that the same user may not come back using the same browser or device, in which case it's impossible to make sure they get the same experience.
-
-If you want [everyone in an account to get the same experience](/docs/feature-management-experimentation/feature-management/best-practices/best-practices-when-targeting-an-account-or-organization) you can use an account ID.  In that case, if you do 50/50 distribution keep in mind that because the accounts will be randomly distributed it's possible that more big accounts (with lots of individual users) will fall into one side of that 50/50 feature flag.  For experimentation purposes that means the actual end user count could be much higher than 50% on that side.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule.md b/docs/feature-management-experimentation/40-feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule.md
deleted file mode 100644
index 7c7ac694ea9..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: How can I override individually targeted users with a superseding targeting rule?
-sidebar_label: How can I override individually targeted users with a superseding targeting rule?
-sidebar_position: 12
----
-
-### Question
-
-I am using a segment in the set individual targets section to give an `on` treatment to users with a certain user IDs. However, if one of those users is on a very slow network (3G or below) I need to give that user the `off` treatment, even if they are in my segment of individually targeted users.  How can we get the treatment for network speed to override the treatment?
-
-### Answer
-
-The conditions or rules are applied from top to bottom, so the higher the rule on the page, the higher-order it will be applied.
-
-![](https://help.split.io/hc/article_attachments/15734176780429)
-
-If you have a rule to include the user by name or by segment, create a rule above that to exclude any user with a specific attribute.  You can use segments or user IDs in targeting rules just as you would in an individually targeted section, but with greater flexibility.
-
-In the Set targeting rules section, select the attribute that would override the individually targeted  users.  You would then select a segment, or user IDs, as the Else If part of the rule.
-
-![](https://help.split.io/hc/article_attachments/15734297213197)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/how-can-you-tell-what-treatment-a-user-sees.md b/docs/feature-management-experimentation/40-feature-management/faqs/how-can-you-tell-what-treatment-a-user-sees.md
deleted file mode 100644
index 9ad64c1afd3..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/how-can-you-tell-what-treatment-a-user-sees.md
+++ /dev/null
@@ -1,51 +0,0 @@
----
-title: How can you tell what treatment a user sees?
-sidebar_label: How can you tell what treatment a user sees?
-sidebar_position: 4
----
-
-There are three primary use cases for determining who is seeing which treatment:
-
-## Use Case 1 - What a user sees whenever they log back in?
-
-This question is often asked to validate the rollout plan when a feature flag is changed. You can answer this question by going to the Key page.  Here's an example:
-
-![customer-dashboard.png](https://help.split.io/hc/article_attachments/15872105262861)
-
-In this image, you can see that the user will see the 'on' treatment for the admin_panel_access flag every time they log in, based on the current rollout plan.
-
-### Caveats:
-
-* This view will only show you treatments for feature flags that are not using [attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes) for targeting. This is because for data privacy reasons, Split does not copy attribute values to the Split cloud. So, we do not know the value of a specific attribute for a given user key (user ID).
-* The key is just for demonstration purposes. It will typically be a database id or a cookie id.
-
-### How to get to the key page:
-
-* You can get to the key page either by searching for that user key in the search box near the top left corner of the app, above (My work).
-* You can also click on the key id in Live Tail, once paused, or the Data hub to get to the key page. 
-
-## Use Case 2 - What treatments is a user seeing right now for one or more feature flags?
-
-This question is often asked when debugging the live experience of a customer or when making changes to a feature flag to see which users are seeing a particular treatment.
-
-You can answer this question by going to Data hub. See this example:
-
-![data_hub-query.png](https://help.split.io/hc/article_attachments/15872511625229)
-
-Here, you are searching for a specific key (say user ID).  Split fetches the live treatments that are being assigned right now.
-
-### Caveats:
-
-* Since this is a live tail of logs, this query will only show you results starting from when you issued the query. It will not show you events from the past. If you want to query historical data you can use the Data export tab in Data hub.
-* Given the size of the data we are searching for, there is an initial lag in the query response of about 10 seconds.
-
-### How to get to Data hub?
-
-* You can click on the Data hub in the navigation to get to this page.
-* If you are on a feature flag page, you can also click on Live tail tab to get a filtered view of impressions for that specific flag.
-
-As noted above, you can get to the key page from the Data Hub or the Live Tail tab by pausing the query and clicking on the key.
-
-## Use Case 3 - What treatments was a user shown in the past?
-
-This is a common question for support teams. You can answer this question by going to the Data export tab of Data hub as shown in the picture above.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/how-do-i-maintain-the-state-of-a-users-treatment-even-if-the-rollout-plan-changes.md b/docs/feature-management-experimentation/40-feature-management/faqs/how-do-i-maintain-the-state-of-a-users-treatment-even-if-the-rollout-plan-changes.md
deleted file mode 100644
index 9c0d62db708..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/how-do-i-maintain-the-state-of-a-users-treatment-even-if-the-rollout-plan-changes.md
+++ /dev/null
@@ -1,87 +0,0 @@
----
-title: How do I maintain the state of a user's treatment, even if the rollout plan changes?
-sidebar_label: How do I maintain the state of a user's treatment, even if the rollout plan changes?
-sidebar_position: 8
----
-
-How do I ensure that once a user (or any traffic key) is given a treatment, they always receive the same treatment, irrespective of any changes to the rollout plan?
-
-The goal is to maintain state so that a user is always exposed to the same thing they saw the first time they visited, even if a change to the rollout plan would otherwise flip the user to another treatment. A sample use case is an offering, such as a loan application or promotional discount, that needs to remain consistent.
-
-## Split's targeting engine
-
-Split is designed to be imperceptibly fast in computing treatments. In Java, for instance, treatments are computed on the order of a few hundred microseconds. This design principle means Split can be used in the most performance-intensive components of your application.
-
-To achieve this speed, Split is designed to not retain state. Specifically, after Split serves a treatment to a user, it does not “remember” what was served. Given the scale of our customer use cases, retaining the combination of customer/key/flag/treatment will negatively impact the requirement for speed in our SDKs.
-
-Split ensures that we give end-users a “sticky” experience and don’t change treatments because the SDK uses a deterministic hashing algorithm. You can understand the details [here](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience). As long as a feature flag’s targeting definition does not change, the user gets the exact same treatment every single time.
-
-Of course, if you make changes to the feature flag targeting definition, some users may be given a different treatment by moving from one treatment to another. This could happen if they fall under a different rule or if the percentages in a rule are changed. That is the intent for an overwhelming number of use cases but is not ideal in some scenarios.
-
-## The solution
-
-There are two ways to 'maintain state': In both cases we use attributes. In the first, the attribute used is the creation date. In the second, the developer maintains the state and passes it as an attribute. The former relies on a date and does not require you to store state, while the latter requires logging the state of the treatment and passing it as the attribute.
-
-**Note: that in both cases using the “kill” switch will void both options.**
-
-For purposes of this article, we'll use a loan application as an example. 
-
-### Solution 1: Use the creation date
-
-Create targeting rules and pass a date as an attribute that represents the date the application was first rendered and passed at runtime to split your traffic.
-
-When you change your targeting rules use date matchers with the date attribute so all applications rendered after that date receive the new allocation.
-
-Using this methodology you can avoid storing the application state and instead store and pass the date the application was originated. In the below example, the application_creation_date is an attribute that you can pass when calling getTreatment with the application ID to tell Split the date the application is being generated.
-
-Let's say we start the new program on November 1st. On day 1 we want v1_application=90% and v2_application=10%. You can set up the targeting rule as
-
-1[how_do_I_maintain_the_state_of_a_users_treatment_one.png](https://help.split.io/hc/article_attachments/30833421020173)
-
-On the 5th we want to split the traffic so that v1_application=80% and v2_application=20%. You can update your targeting rules as
-
-![how_do_I_maintain_the_state_of_a_users_treatment_two.png](https://help.split.io/hc/article_attachments/30833432085389)
-
-In this scenario, the create date for an application will not change and it will continue to meet the first targeting rule. Only new applications will fall into the second targeting rule.
-
-Finally, on day 14, we've learned that v2 is performing as expected so we set the ratios to v1_application=0% and v2_application=100%.
-
-![how_do_I_maintain_the_state_of_a_users_treatment_three.png](https://help.split.io/hc/article_attachments/30833421026445)
-
-As noted above, the creation date does not change and existing applications will meet the first or second targeting rules.  Only new applications will fall into the third targeting rule.
-
-The downside to this approach is that it works if there are a limited number of targeting definition changes.  It would be tedious to remember to change the date matchers with each rollout change and is error-prone given the manual nature.
-
-This example will work with Split’s experimentation capabilities, showing the statistical significance for each targeting criterion above.
-
-### Solution 2: Storing which treatment was assigned to an application
-
-With this solution, the developer must maintain state somewhere. While, by design, Split can’t keep state using the SDKs, you can retain the application_id/flag/treatment combination by storing this data in, for example, a cookie or database. You could also store the data in a Split segment using the Admin API, assuming the number of unique users is not too large.
-
-If you can store this state somewhere in a cookie or database you can configure the feature flag once and not worry about changing date matchers with each change.
-
-![how_do_I_maintain_the_state_of_a_users_treatment_four.png](https://help.split.io/hc/article_attachments/30833421028877)
-
-In this example, application_seen is the attribute to pass when calling getTreatment to tell Split if an applicant was already given a treatment. The value can be null or empty for first time applications without causing errors. Here’s how you’d make the call in Java:
-
-```java
-Map<String, Object> attributes = new HashMap<String, Object>();
-attributes.put("application_seen", "v1_application"); // fetch from db
-String treatment = client.getTreatment("USER_ID", "SPLIT_NAME", attributes);
-```
-
-Using this approach, any changes you make to the default rule will not affect users that had already been assigned a treatment.
-
-If the number of applicants you see for the duration of a feature flag is small enough (10k or less), you can create a segment within Split. See the [segment API documentation](https://docs.split.io/reference#segment-1). This will obviate the need to retain state in a cookie or database.
-
-In this case, create a segment per flag/treatment combination. Based on the treatment assigned you would store the ID in the appropriate segment.  Then you could set up your feature flag using individual targets. 
-
-![how_do_I_maintain_the_state_of_a_users_treatment_five.png](https://help.split.io/hc/article_attachments/30833432095885)
-
-or you use the segment in a rule if more flexibility is needed
-
-![how_do_I_maintain_the_state_of_a_users_treatment_six.png](https://help.split.io/hc/article_attachments/30833432096397)
-
-This involves a bit of work. Using the cookie or database solution is preferred over using segments.
-
-Solution #2 doesn't fully leverage Split’s experimentation capabilities and is preferable for basic rollouts. You can measure changes across rules, but not see the statistical significance for each targeting rule since each targeting rule is serving a single treatment.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment.md b/docs/feature-management-experimentation/40-feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment.md
deleted file mode 100644
index a93c3902617..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Is there a way to cap the number of users participating in an experiment?
-sidebar_label: Is there a way to cap the number of users participating in an experiment?
-sidebar_position: 11
----
-
-### Question
-
-Is there a way to limit the number of users participating in an experiment?
-
-### Answer
-
-Within the feature flag definition, there is no cap feature, however, it's possible to limit the number of user in approximate way using the Limit exposure feature.
-
-For example, if the total unique users in the site or page used in the experiment are 1 million, then it's possible to limit the exposure to 15%, which will allow only 150k of the unique users into the experiment.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/useful-regex-examples-for-targeting.md b/docs/feature-management-experimentation/40-feature-management/faqs/useful-regex-examples-for-targeting.md
deleted file mode 100644
index 217f137d74d..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/useful-regex-examples-for-targeting.md
+++ /dev/null
@@ -1,84 +0,0 @@
----
-title: Useful regex examples for targeting
-sidebar_label: Useful regex examples for targeting
-sidebar_position: 10
----
-
-Split targeting rules support matching regex (regular expression) to serve specific treatment, Regex is a powerful tool to filter a wide spectrum of population with one line of text. We recommended that you test your regex on an external tool to verify it works correctly. Here is one [suggested site](https://regex101.com/).
-
-Below are a few useful examples.
-
-## Target specific app major version
-
-To target users with app version greater than or equal to 4.5 to get the `on` treatment, this regex could be used for matching the `appVersion` attribute:
-
-```
-(\[5-9\]\\.\[0-9\]|\[4\]\\.\[5-9\]).*
-```
-
-![](https://help.split.io/hc/article_attachments/15726820984077)
-
-You can use the `matches` operator in the targeting rule editor within the Split user interface to match the value of a passed attribute (such as an app version) to a regular expression and specify treatment assignments for any versions that match the expression. 
-
-This approach also works when versions are formatted in `Major.Minor` and you want a specific treatment for versions below a certain threshold (for example, versions below `2.1.0`).
-
-Example of passing the `appVersion` attribute for the JavaScript SDK:
-
-```javascript
-var attributes = {appVersion: "4.567.33"};
-```
-
-## Target all population of specific domain of user emails
-
-For this example, to serve the `on` treatment for all employees of split.io, we can use the Regex below for `email` attribute:
-
-@split\.io$
-
-![](https://help.split.io/hc/article_attachments/15726851838093)
-
-Example of passing the email attribute for the JavaScript SDK:
-
-```javascript
-var attributes = {email: "bilal@split.io"};
-```
-
-## Target users on Chrome version 20 and later
-
-This example we are serving the `on` treatment to Chrome users only. However, we want to only use Chrome versions 20 and later for compatibility reasons. The attribute passed is userAgent:
-
-Chrome\\/[2-9][0-9]\\.
-
-![](https://help.split.io/hc/article_attachments/15726938445709)
-
-Example for the JavaScript SDK:
-
-```javascript
-var attributes = {userAgent: "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36"};
-```
-
-## Target English speaking users world wide
-
-An experiment is designed for English speaking users in any country by detecting the default language setting in the browser, the attribute passed is navigatorLanguage
-en-.
-
-![](https://help.split.io/hc/article_attachments/15726949310861)
-
-Example for the JavaScript SDK:
-
-```javascript
-var attributes = {navigatorLanguage: navigator.language};
-```
-
-## Target specific URL
-
-To target an experiment that applies only to users that land on a specific URL, the extracted URL can be passed as an attribute to the targeting rule:
-
-http:\\/\\/mysite\\.com\\/my_url
-
-![](https://help.split.io/hc/article_attachments/15727006393485)
-
-Example for the JavaScript SDK:
-
-```javascript
-var attributes = {url: window.location.href};
-```
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/what-are-attributes-in-the-admin-api-vs-custom-attributes-in-targeting-rules.md b/docs/feature-management-experimentation/40-feature-management/faqs/what-are-attributes-in-the-admin-api-vs-custom-attributes-in-targeting-rules.md
deleted file mode 100644
index 2720a48f49c..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/what-are-attributes-in-the-admin-api-vs-custom-attributes-in-targeting-rules.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: What are attributes in the Admin API vs custom attributes in targeting rules?
-sidebar_label: What are attributes in the Admin API vs custom attributes in targeting rules?
-sidebar_position: 15
----
-
-### Question
-
-When using the [Admin API](https://docs.split.io/reference#identities-overview) that create attributes to assign a value (identity) to client IDs, is this the same attribute used in the feature flag definition from the user interface?
-
-### Answer
-
-The attributes and user ID in the Split API is completely different from the feature flag definition attributes, they have no connection and do not affect each other.
-
-The Split API attributes are used to assign any existing user ID with identities that show up when a user hovers the mouse on a user ID in the Result tab in the Split user interface. They are not involved in the targeting or deciding treatments.
-
-On the other hand, the targeting rules attributes, are usually entered through the user interface, Split accepts any text used for attributes.
-
-When the SDK calls `getTreatment` method, the code need to create the attribute object, insert the same attribute specified in the targeting rules with their corresponding values and pass the attribute object to the `getTreatment` method.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment.md b/docs/feature-management-experimentation/40-feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment.md
deleted file mode 100644
index 30c21c2a042..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment.md
+++ /dev/null
@@ -1,26 +0,0 @@
----
-title: What is the difference between Default Rule and Default Treatment?
-sidebar_label: What is the difference between Default Rule and Default Treatment?
-sidebar_position: 5
----
-
-### Question
-
-Given the following scenario: A feature flag with a simple on and off feature flag. if I allocate 10% of my traffic through the traffic allocation feature, then I assume that only 10% of people will be presented with this feature flag, however, how does the 'set the default rule' and 'set the default treatment' options come into play?
-
-### Answer
-
-When calculating the treatment by calling the SDK's getTreatment you are calling it for 100% of the people who hit the feature flag.  You need to make sure that everyone gets some sort of treatment, whether OFF or ON or something else.  
-
-Limit traffic limits the number of users who participate in the targeting rules and the default rule.  In other words, if you set Traffic Allocation to 10% then 10% of the users will be subject to the Rules you set up.  The remaining 90% will get the default treatment.
-
-If you have a two versions of a new feature and you want to expose either to only 10% of you population you might create three treatments: version1, version2, unallocated, where unallocated doesn't expose them to any version of the new feature.  You could then set the Traffic Allocation to 10% and then maybe set the Default Rule to 50/50 for version1/version2.  You'd set the Default Treatment to unallocated.
-
-
-Traffic Allocation is useful for moving users from an unallocated state into rules when doing experimentation and/or when you have more than just two treatments.  If only two treatments, it's also useful if you have a lot of rules and you want to increase participation across all rules in a consistent fashion.  It's also useful in excluding users from the metrics calculated if you're using Split for experimentation.  In cases where you have just two treatments and you are rolling out to a percentage, traffic allocation doesn't really buy you anything.
-
-For example, the following examples have the same result in terms of traffic distribution. 
-
-In one, 90% of the users are getting Off as a result of the default rule and NO users are going to the default treatment.
-
-In the next one, 90% of the users are getting OFF as a result of going to the default treatment.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/why-are-some-users-double-bucketed.md b/docs/feature-management-experimentation/40-feature-management/faqs/why-are-some-users-double-bucketed.md
deleted file mode 100644
index af2902b9dda..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/why-are-some-users-double-bucketed.md
+++ /dev/null
@@ -1,70 +0,0 @@
----
-title: Why are some user Ids double bucketed?
-sidebar_label: Why are some user Ids double bucketed?
-sidebar_position: 14
----
-
-### Issue
-
-Using Javascript SDK, some percentage of User Ids are double bucketed: the same User Id is processed in the Control block, and at a later call in an actual treatment block.
-
-### Root Cause
-
-One possible root cause is Javascript SDK engine is completing fetching the treatments after the requestTimeoutBeforeReady parameter or the readyTimeout parameter, which will fire the browser event SDK_READY_TIMED_OUT.
-
-This is more likely for mobile users with a slow internet connection.
-
-If the Javascript code does not handle the event properly, for example, it only raises an error and exits:
-
-```javascript
-var client = SdkFactory.client();this.clientReady = new Promise(function(resolve,reject) {
-   client.once(client.Event.SDK_READY, function() {
-      resolve();
-   });
-   client.once(client.Event.SDK_READY_TIMED_OUT, function() {
-      reject(new Error("Client SDK Ready Timed Out"));
-   });
-});
-```
-
-There are two possible events here:
-
-1. If the SDK_READY event comes first, the code resolves that promise and everything is ok.
-2. If the SDK_READY_TIMED_OUT event comes first, the code rejects that promise and it will always be rejected, which means that if you attach a success callback on that function (this.clientReady in the example) always calling any .catch() callback re-attached to it, even if the SDK eventually gets ready. 
-
-If you just add a catch function that does not return nor throw an error, the custom wrapper may continue executing with an SDK that's NOT ready, thinking it's ready. For example:
-
-```javascript
-this.clientReady.catch(() => {}).then(() => { do some stuff with the SDK })
-```
-
-Notice the catch function does not return nor throws an error, swallowing the error or recovering the promise chain. If you don't throw nor return an error, the JavaScript engine will consider the callback chain as recovered (each callback you add returns a new promise). Not throwing an error on a catch is the way JavaScript developers have to handle errors and keep the flow executing.
-
-If you do that, you won't have a trustworthy signal of the SDK being ready on that promise.
-
-Going back, even if you are getting a SDK_READY_TIMED_OUT event, the SDK may get ready a few ms later, emit the SDK_READY event and work as expected. You should handle ready event, and timeout event only when they want you actually want to handle it. 
-
-## Solution
-
-There are two possible solutions here:
-
-1. Adjust the SDK Timeout and retry parameters
-
-  This setting will allow the SDK to try twice before giving up in the first initial attempt. It will try again after 30-60 seconds. The 5 seconds is proposed as a worst-case scenario of how long it takes to fetch the definitions. It's faster to wait for the first fetch, so setting the requestTimeoutBeforeReady and readyTimeout to the same or similar values is harmless. (readyTimeout would be the overall value, including the retries, while the requestTimeoutBeforeReady is the timeout per request)
-
-  ```javascript
-      startup: {
-        requestTimeoutBeforeReady: 5,   // 5 seconds
-        readyTimeout: 5,                // 5 seconds
-        retriesOnFailureBeforeReady: 2, // 2 retries
-      }
-  ```
-
-2. Use the LOCALSTORAGE in the browser to store the Splits and mySegments data. This will fetch only for changes since the last good fetch on that browser. This makes it much faster for the SDK to be in the ready state on subsequent loads since it will remove the need of downloading all data on every load. See the following doc link [https://docs.split.io/docs/javascript-sdk-overview#section-configuration](https://docs.split.io/docs/javascript-sdk-overview#section-configuration), but you only need to specify the storage entry on your SDK configuration. Try to use a custom prefix that represents your organization or project, since it's the tool for preventing data collision between different projects.
-
-  ```javascript
-   storage: {
-    type: 'LOCALSTORAGE',
-    prefix: 'MYPREFIX'
-  },
-  ```
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/why-might-the-same-user-id-get-different-treatments.md b/docs/feature-management-experimentation/40-feature-management/faqs/why-might-the-same-user-id-get-different-treatments.md
deleted file mode 100644
index 20b8c95a30f..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/faqs/why-might-the-same-user-id-get-different-treatments.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Why might the same user id get different treatments?
-sidebar_label: Why might the same user id get different treatments?
-sidebar_position: 16
----
-
-### Question
-
-Why are impressions for the same user id showing different treatments? Shouldn't the same user id should always receive the same treatment throughout the same version of the experiment?
-
-### Answer
-
-The most likely explanation for this is that the definition of the feature flag has a targeting rule that checks an attribute value when assigning the treatment where if the value of the attribute _state_ passed to getTreatment is _CO_, then the off treatment is assigned, otherwise the default rule applies and the on treatment is assigned.
-
-To see if this is indeed the case, make sure that on the Live tail tab you have elected to show the Targeting rule field of the impression (this is the case by default). Then it will be clear why a different treatment was assigned.
-
-In this case, the attribute checked in the targeting rule is the deciding factor as to which treatment a user receives. If the value of that attribute is different between the two calls to getTreatment, a different treatment could be assigned.
-
-Of course if the version showed for the impressions is different, that is another possibility: the definition of the feature flag changed from one version to another in a way that caused the user to be assigned a different treatment.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md b/docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md
new file mode 100644
index 00000000000..02766d9c400
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md
@@ -0,0 +1,96 @@
+---
+title: Maintaining Consistent Treatments
+sidebar_label: Maintaining Consistent Treatments
+description: "Learn how to ensure users maintain the same treatment over time, even if rollout plans change. Explore practical approaches using attributes, creation dates, and state storage to maintain treatment consistency."
+sidebar_position: 25
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/how-do-i-maintain-the-state-of-a-users-treatment-even-if-the-rollout-plan-changes
+---
+
+## Overview
+
+How do I ensure that once a user (or any traffic key) is given a treatment, they always receive the same treatment, irrespective of any changes to the rollout plan?
+
+The goal is to maintain state so that a user is always exposed to the same treatment they saw the first time, even if the rollout plan changes. This is important for use cases like loan applications or promotional discounts that need to remain consistent.
+
+
+## Split's Targeting Engine
+
+Split is designed for imperceptibly fast treatment computation. For example, in Java, treatments are computed in just a few hundred microseconds. To achieve this speed, Split does **not** retain state. After serving a treatment, it does not “remember” what was served.
+
+Instead, Split uses a deterministic hashing algorithm to assign treatments. This ensures a [“sticky” experience](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience/) where, as long as the feature flag targeting definition remains unchanged, users receive the **same** treatment every time.
+
+However, if the targeting rules or percentages change, some users may receive different treatments. This behavior is intentional and appropriate for most use cases, but not all.
+
+## The Solution
+
+There are two primary ways to maintain treatment state, both leveraging attributes:
+
+- **Solution 1:** Use a creation date attribute — no need to store state externally.
+- **Solution 2:** Store the assigned treatment externally and pass it as an attribute.
+
+> **Note:** Using the “kill” switch will void both of these options.
+
+For the sake of examples, we use a loan application scenario.
+
+### Solution 1: Use the Creation Date
+
+Create targeting rules that use a date attribute representing when the application was first rendered. Pass this date at runtime to Split to segment your traffic.
+
+- When updating rollout plans, use date matchers to ensure only new applications (after a certain date) receive the new treatment.
+- This avoids storing treatment state and relies only on storing the application’s origin date.
+
+Example workflow:
+
+- **Day 1 (Nov 1):**  
+  90% get `v1_application`, 10% get `v2_application`.  
+  *(Targeting rule with date matcher for Nov 1)*  
+  ![Day 1 targeting rule example](./static/create-date-1.png)
+
+- **Day 5:**  
+  Traffic split adjusted to 80% `v1_application` and 20% `v2_application`.  
+  ![Day 5 targeting rule example](./static/create-date-2.png)
+
+- **Day 14:**  
+  `v1_application` set to 0%, `v2_application` set to 100%.  
+  ![Day 14 targeting rule example](./static/create-date-3.png)
+
+Existing applications continue to match their original date-based rule and maintain treatment. New applications fall into updated rules.
+
+**Limitations:**  
+This method works well if targeting changes are infrequent. Manual updates to date matchers can be tedious and error-prone.
+
+### Solution 2: Store Assigned Treatment Externally
+
+Developers maintain treatment state externally — e.g., in a cookie or database — storing the application ID, flag, and treatment.
+
+You then pass this stored treatment as an attribute when calling `getTreatment`.
+
+```java
+Map<String, Object> attributes = new HashMap<String, Object>();
+attributes.put("application_seen", "v1_application"); // fetched from DB
+String treatment = client.getTreatment("USER_ID", "SPLIT_NAME", attributes);
+```
+
+This approach ensures users retain the same treatment despite changes to rollout plans or default rules.
+
+If you have fewer than ~10k users, you can also:
+
+- Create a Split segment per flag/treatment combination (using Admin API).
+- Assign user IDs to the appropriate segment based on stored treatment.
+- Use individual targeting rules with these segments.
+
+![Using segments for treatment targeting](./static/create-date-4.png)
+
+Or use segments as part of targeting rules for more flexibility:
+
+![Segment in targeting rule example](./static/create-date-5.png)
+
+This method requires additional management and is less integrated with Split’s experimentation features. It’s better suited for simple rollouts rather than experiments.
+
+## Summary
+
+* Solution 1 (creation date): Lightweight, no external storage, works well if targeting changes are infrequent. Supports experimentation insights.
+* Solution 2 (external state storage): Requires manual state management, suited for simple rollouts, less suitable for experiments.
+
+Both methods help maintain a consistent user experience by keeping users “sticky” to their original treatment.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/segments.md b/docs/feature-management-experimentation/40-feature-management/segments.md
index 90a51b28baf..df8df084819 100644
--- a/docs/feature-management-experimentation/40-feature-management/segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/segments.md
@@ -5,6 +5,7 @@ description: "Learn how to create, delete, and manage segments in Harness FME."
 sidebar_position: 6
 redirect_from:
 - /docs/feature-management-experimentation/feature-management/faqs/how-to-delete-a-segment-with-all-its-keys
+- /docs/feature-management-experimentation/feature-management/best-practices/csv-file-comma
 ---
 
 ## Overview
@@ -175,19 +176,19 @@ Follow these steps to delete all keys from the segment and delete the segment:
 1. Locate the down arrow on top of the checkboxes column.
 1. Click on it to display two menu items.
    
-   ![](https://help.split.io/hc/article_attachments/15726171868813)
+   ![](./static/delete-1.png)
 
 1. Select all records from there. The **Delete** button appears.
 
-   ![](https://help.split.io/hc/article_attachments/15726281778445)
+   ![](./static/delete-2.png)
 
 1. Once the keys are deleted, delete the segment definition by clicking on the Ellipses icon in the **Definition** section and selecting **Delete definition**.
    
-   ![](https://help.split.io/hc/article_attachments/15726343693453)
+   ![](./static/delete-3.png)
 
 1. Click on the Ellipses icon above **Owners** and select **Delete**.
    
-   ![](https://help.split.io/hc/article_attachments/15726349353613)
+   ![](./static/delete-4.png)
 
 ## CSV import file format
 
@@ -201,6 +202,24 @@ id2
 id3
 ```
 
+## Uploading user IDs with commas in CSV files
+
+When uploading CSV files with user IDs that contain commas, do not manually add double quotes around the IDs. The CSV import automatically treats each line as a unique user ID and adds any necessary quotes during import. For example, instead of:
+
+```
+"user1, key1"
+"user2, key2"
+```
+
+use:
+
+```
+user1, key1
+user2, key2
+```
+
+This ensures the user IDs are imported correctly into the segment.
+
 ## Checking if a user is in a segment
 
 The Admin API does not include an endpoint to check if a user ID (or key) exists in a specific segment. Instead, you can use the mobile and browser SDK's `mySegments` endpoint, which returns the list of segment names associated with a given user key.
diff --git a/docs/feature-management-experimentation/40-feature-management/static/create-date-1.png b/docs/feature-management-experimentation/40-feature-management/static/create-date-1.png
new file mode 100644
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diff --git a/docs/feature-management-experimentation/40-feature-management/faqs/static/dynamic-configuration-section.png b/docs/feature-management-experimentation/40-feature-management/static/dynamic-configuration-section.png
similarity index 100%
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diff --git a/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md b/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md
index afd87deb6a9..a3399e6e5e3 100644
--- a/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md
+++ b/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md
@@ -5,6 +5,7 @@ description: "Learn how to add custom attributes to create dynamic targeted feat
 sidebar_position: 9
 redirect_from:
 - /docs/feature-management-experimentation/feature-management/faqs/does-my-sdk-version-support-semver
+- /docs/feature-management-experimentation/feature-management/faqs/what-are-attributes-in-the-admin-api-vs-custom-attributes-in-targeting-rules/
 ---
 
 import Tabs from '@theme/Tabs';
@@ -118,6 +119,18 @@ To create multiple custom attributes that will appear as User Attributes in your
    ```
 6. When the file upload is complete, click **Save**. The new attributes are created and displayed on the Attributes page.
 
+### Difference Between Admin API Attributes and Targeting Rule Custom Attributes
+
+When using the [Admin API](https://docs.split.io/reference#identities-overview) to create attributes that assign values (identities) to client IDs, is this the same attribute used in the feature flag targeting rules from the user interface?
+  
+No, these are two distinct concepts:
+
+- **Admin API Attributes:** These attributes are used to assign identities or additional information to user IDs in the Split Admin API. They appear in the Split UI (e.g., when hovering over a user ID in the Results tab) but do **not** influence feature flag targeting or treatment assignment.
+
+- **Targeting Rule Custom Attributes:** These are attributes defined and used specifically within feature flag targeting rules to determine treatment assignment. When the SDK calls `getTreatment`, you must pass the relevant custom attributes with their corresponding values for proper evaluation against the targeting rules.
+
+Understanding this distinction is important to avoid confusion when managing user data versus defining targeting logic.
+
 ### Creating custom attributes or writing custom attribute values using API endpoints
 
 You can create custom attributes for use in feature flag targeting rules by using the following Split API endpoints:
@@ -172,12 +185,102 @@ You can also directly click within the IF field, type a new custom attribute ID,
     Serve the `on` treatment for users with custom attribute `registered_date` on or after a specified date:
 
     ![](./static/target-with-custom-attributes-using-custom-attributes-04-date.png)
-  
+
+## Using regex with custom attributes
+
+Split targeting rules support matching values using regular expressions (regex), which provides a powerful way to filter a wide range of users with a single targeting rule.
+
+:::tip
+We recommend testing your regex patterns on external tools like [regex101.com](https://regex101.com/) to ensure they work as expected before applying them in Split.
+:::
+
+Below are some useful regex examples for targeting specific user attributes:
+
+### Target specific app major version
+
+To target users with app version greater than or equal to 4.5 to get the on treatment, this regex could be used for matching the `appVersion` attribute:
+
+```
+(\[5-9\]\\.\[0-9\]|\[4\]\\.\[5-9\]).*
+```
+
+![](./static/targeting-1.png)
+
+You can use the `matches` operator in the targeting rule editor within the Split user interface to match the value of a passed attribute (such as an app version) to a regular expression and specify treatment assignments for any versions that match the expression.
+
+This approach also works when versions are formatted in `Major.Minor` and you want a specific treatment for versions below a certain threshold (for example, versions below `2.1.0`).
+
+Example of passing the `appVersion` attribute for the JavaScript SDK:
+
+```javascript
+var attributes = {appVersion: "4.567.33"};
+```
+
+### Target all population of specific domain of user emails
+
+For this example, to serve the `on` treatment for all employees of split.io, we can use the Regex below for `email` attribute:
+
+```
+@split.io$
+```
+
+![](./static/targeting-2.png)
+
+Example of passing the email attribute for the JavaScript SDK:
+
+```
+var attributes = {email: "bilal@split.io"};
+```
+
+### Target users on Chrome version 20 and later
+
+This example we are serving the on treatment to Chrome users only. However, we want to only use Chrome versions 20 and later for compatibility reasons. The attribute passed is userAgent:
+
+```
+Chrome\/[2-9][0-9]\.
+```
+
+![](./static/targeting-3.png)
+
+Example for the JavaScript SDK:
+
+```
+var attributes = {userAgent: "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36"};
+```
+
+### Target English speaking users world wide
+
+An experiment is designed for English speaking users in any country by detecting the default language setting in the browser, the attribute passed is `navigatorLanguage en-`.
+
+![](./static/targeting-4.png)
+
+Example for the JavaScript SDK:
+
+```javascript
+var attributes = {navigatorLanguage: navigator.language};
+```
+
+### Target specific URL
+
+To target an experiment that applies only to users that land on a specific URL, the extracted URL can be passed as an attribute to the targeting rule:
+
+```
+http:\/\/mysite\.com\/my_url
+```
+
+![](./static/targeting-5.png)
+
+Example for the JavaScript SDK:
+
+```javascript
+var attributes = {url: window.location.href};
+```
+
 ## How feature flag targeting rules with custom attributes are evaluated
 
 This section explains how a feature flag targeting rule with a custom attribute is evaluated in your source code, in the following cases:
 
-##### The custom attribute value is not provided
+### The custom attribute value is not provided
 
 The feature flag targeting rule containing the custom attribute does not result in a match.
 
@@ -190,7 +293,7 @@ else 100% : off
 
 If the value for the age attribute is not provided in code in the attributes map passed to the getTreatment call, the matcher in the first condition `age <= 20` evaluates to **false**. The `else` condition then evaluates to **true**, resulting in the `off` treatment.
 
-##### The custom attribute value is not the correct type
+### The custom attribute value is not the correct type
 
 The feature flag targeting rule containing the custom attribute does not result in a match.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting-an-account.md b/docs/feature-management-experimentation/40-feature-management/targeting-an-account.md
new file mode 100644
index 00000000000..037608fa12d
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/targeting-an-account.md
@@ -0,0 +1,40 @@
+---
+title: Targeting an Account or Organization
+sidebar_label: Targeting an Account or Organization
+description: "Learn how to effectively target feature flags by accounts or organizations (such as schools, hospitals, departments, or any group with a unique key) instead of individual users."
+sidebar_position: 23
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/best-practices/best-practices-when-targeting-an-account-or-organization/
+---
+
+## Overview
+
+Traffic types that target using a key for accounts or organizations (or schools, hospitals, or other entities)—as opposed to individual users—are often used for managing entitlements or B2B applications. You can also target based on departments, plants, locations, or any level of granularity for which you have an associated key. In this document, we’ll use "account" as a proxy term for all of the above.
+
+When you want consistency of experience for all users within a single account, use the account traffic type. By providing a single ID (the account ID) you ensure consistent treatment across all users without needing complex user-level data.
+
+## Measuring Impact in Experiments
+
+If you plan to measure the impact of a feature rollout by accounts using experimentation, avoid metrics that `SUM` or `COUNT` activity across users. For example:
+
+* Account A with 1,000 users and
+* Account B with 2 users
+
+A `COUNT` or `SUM` metric will disproportionately favor Account A, skewing results.
+
+Instead, use metrics that calculate `AVERAGE` or `PERCENTAGE`, which normalize data across accounts for fairer comparison.
+
+## Rolling Out by User vs. Account
+
+If your organization is comfortable with some variability in experience consistency within accounts, you can roll out features using the user traffic type. This enables user-level experimentation and metrics such as `SUM`, `COUNT`, `AVERAGE`, and `PERCENTAGE`.
+
+Alternatively, you can roll out by user but use the company as a custom attribute to deliver consistent experiences within each organization. This approach allows user-level analysis while maintaining organizational consistency. However, be aware that companies with vastly different user counts can affect distribution control and metric interpretation.
+
+## Tradeoffs and Strategy
+
+These targeting strategies have tradeoffs that teams should weigh carefully. You can:
+
+* Roll out some features by account to ensure experience consistency, and
+* Roll out others by user for detailed experimentation.
+
+Split enables you to manage and combine these approaches to best fit your business needs.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md b/docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md
new file mode 100644
index 00000000000..ed2ea627056
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md
@@ -0,0 +1,69 @@
+---
+title: Viewing User Treatments
+sidebar_label: Viewing User Treatments
+description: "Learn how to determine which feature flag treatments a user currently sees, what they will see on subsequent visits, and how to review their past treatment history using the Split dashboard and data tools."
+sidebar_position: 24
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/how-can-you-tell-what-treatment-a-user-sees
+  - /docs/feature-management-experimentation/feature-management/faqs/why-might-the-same-user-id-get-different-treatments/
+---
+
+## Overview
+
+There are three primary use cases for determining which treatment a user sees:
+
+## Use Case 1 – What a user sees whenever they log back in
+
+This question is often asked to validate the rollout plan when a feature flag is changed. You can answer this by going to the **Key page**. Here's an example:
+
+![](./static/use-case-1.png)
+
+In this image, you can see that the user will see the **'on'** treatment for the `admin_panel_access` flag every time they log in, based on the current rollout plan.
+
+### Caveats
+
+- This view only shows treatments for feature flags **not using [attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/)** for targeting.  
+- For data privacy reasons, Split does **not copy attribute values** to the cloud, so it cannot know attribute values for a given user key (user ID).  
+- The key shown is typically a database ID or a cookie ID.
+
+To navigate to the Key page:
+
+1. Search for the user key in the search box near the top left corner of the app (above **My work**).  
+1. Or click on the key ID in **Live Tail** (when paused), or in the **Data hub** to navigate to the Key page.
+
+## Use Case 2 – What treatments is a user seeing right now for one or more feature flags?
+
+This question is common when debugging live customer experiences or when making changes to a feature flag.
+
+You can answer this by going to **Data hub**. Example:
+
+![](./static/use-case-2.png)
+
+Here, you search for a specific key (e.g., user ID). Split fetches the live treatments currently assigned.
+
+### Caveats
+
+- This live tail shows only results starting from when you issued the query; it does **not** include past events.  
+- For historical data, use the **Data export** tab in Data Hub.  
+- There is typically an initial lag of about 10 seconds in query response.
+
+To navigate to the Data Hub:
+
+1. Click on **Data hub** in the navigation.  
+1. From a feature flag page, you can also click the **Live tail** tab for a filtered view of impressions for that flag.  
+1. From Data Hub or Live Tail, you can get to the Key page by pausing the query and clicking on the key.
+
+### Why might the same user ID get different treatments right now?
+
+Sometimes, impressions for the same user ID show different treatments even within the same version of an experiment. This usually happens because the feature flag's targeting rules consider attributes passed at evaluation time.
+
+For example, if a targeting rule assigns the `off` treatment when the `state` attribute equals `CO`, but assigns `on` otherwise, then depending on the attribute value passed to `getTreatment`, the user may receive different treatments.
+
+To confirm this, use the **Live tail** tab and enable the **Targeting rule** field in impressions. This will help clarify which rule assigned the treatment.
+
+Additionally, if the attribute values vary between calls or the flag definition version changes, users might see different treatments.
+
+## Use Case 3 – What treatments was a user shown in the past?
+
+This is a common support question. You can answer this by going to the **Data export** tab of Data Hub.
+
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/index.md b/docs/feature-management-experimentation/60-experimentation/setup/index.md
index ab78af1e3d3..bc67c8e870f 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/index.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/index.md
@@ -1,9 +1,11 @@
 ---
 title: Setup
 sidebar_position: 50
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/is-there-a-way-to-limit-the-number-of-users-in-an-experiment/
 ---
 
-## Create an experiment
+## Overview
 
 ### Interactive guide
 
@@ -56,6 +58,10 @@ The Default treatment could be the same as **off**, or it could be a third treat
 
 ![](../static/users-exposure-limit.png)
 
+#### Capping the number of experiment participants
+
+While there's no direct "cap" feature within a feature flag definition, you can use limit exposure to approximate this. For example, if your experiment's total unique user population is around 1 million, setting the exposure limit to 15% would allow roughly 150,000 unique users to participate. This is useful for risk mitigation or staged rollouts where you want tighter control over the sample size.
+
 ### Using dependencies
 
 However, with this approach each time you increase exposure limit you are changing the feature flag. That means the metrics will reset.  

From b3a9a8f5827f60cba648b6250892bddb1151d43f Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Tue, 12 Aug 2025 18:28:11 -0700
Subject: [PATCH 02/11] Add Index Pages + Update Image References

---
 .../40-feature-management/index.md            | 84 +++++++++++++++++
 .../manage-flags/index.md                     | 20 +++++
 .../maintaining-consistent-treatments.md      | 10 +--
 .../{ => manage-flags}/reallocate-traffic.md  |  4 +-
 .../set-the-alert-baseline-treatment.md       |  6 +-
 .../targeting-an-account.md                   |  0
 .../{ => manage-flags}/use-the-kill-switch.md |  2 +
 .../using-essential-scheduling.md             |  4 +-
 ...sing-flag-sets-to-boost-sdk-performance.md |  4 +-
 .../viewing-user-treatments.md                |  4 +-
 .../customer-dashboard.md                     |  4 +-
 .../{ => monitoring-analysis}/export-data.md  |  4 +-
 .../{ => monitoring-analysis}/impressions.md  |  9 +-
 .../monitoring-analysis/index.md              | 14 +++
 .../{ => monitoring-analysis}/live-tail.md    |  4 +-
 .../set-the-default-treatment.md              | 31 -------
 .../{ => setup}/control-treatment.md          |  2 +
 .../{ => setup}/create-a-feature-flag.md      | 89 ++-----------------
 .../{ => setup}/create-a-rollout-plan.md      |  8 +-
 .../{ => setup}/default-treatment.md          |  5 +-
 ...e-feature-flag-treatments-and-targeting.md | 17 ++--
 .../{ => setup}/dynamic-configurations.md     |  5 +-
 .../{ => setup}/edit-treatments.md            |  4 +-
 .../40-feature-management/setup/index.md      | 18 ++++
 .../40-feature-management/targeting/index.md  | 15 ++++
 .../{ => targeting}/limiting-exposure.md      |  2 +-
 .../{ => targeting}/segments.md               | 26 +++---
 .../{ => targeting}/target-segments.md        |  8 +-
 .../target-with-custom-attributes.md          | 32 +++----
 .../target-with-dependencies.md               |  2 +-
 sidebars.ts                                   | 82 +++++++++++++++--
 31 files changed, 329 insertions(+), 190 deletions(-)
 create mode 100644 docs/feature-management-experimentation/40-feature-management/index.md
 create mode 100644 docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/maintaining-consistent-treatments.md (93%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/reallocate-traffic.md (92%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/set-the-alert-baseline-treatment.md (91%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/targeting-an-account.md (100%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/use-the-kill-switch.md (92%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/using-essential-scheduling.md (94%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/using-flag-sets-to-boost-sdk-performance.md (95%)
 rename docs/feature-management-experimentation/40-feature-management/{ => manage-flags}/viewing-user-treatments.md (98%)
 rename docs/feature-management-experimentation/40-feature-management/{ => monitoring-analysis}/customer-dashboard.md (89%)
 rename docs/feature-management-experimentation/40-feature-management/{ => monitoring-analysis}/export-data.md (97%)
 rename docs/feature-management-experimentation/40-feature-management/{ => monitoring-analysis}/impressions.md (97%)
 create mode 100644 docs/feature-management-experimentation/40-feature-management/monitoring-analysis/index.md
 rename docs/feature-management-experimentation/40-feature-management/{ => monitoring-analysis}/live-tail.md (97%)
 delete mode 100644 docs/feature-management-experimentation/40-feature-management/set-the-default-treatment.md
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/control-treatment.md (96%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/create-a-feature-flag.md (55%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/create-a-rollout-plan.md (99%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/default-treatment.md (93%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/define-feature-flag-treatments-and-targeting.md (96%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/dynamic-configurations.md (97%)
 rename docs/feature-management-experimentation/40-feature-management/{ => setup}/edit-treatments.md (84%)
 create mode 100644 docs/feature-management-experimentation/40-feature-management/setup/index.md
 create mode 100644 docs/feature-management-experimentation/40-feature-management/targeting/index.md
 rename docs/feature-management-experimentation/40-feature-management/{ => targeting}/limiting-exposure.md (97%)
 rename docs/feature-management-experimentation/40-feature-management/{ => targeting}/segments.md (94%)
 rename docs/feature-management-experimentation/40-feature-management/{ => targeting}/target-segments.md (90%)
 rename docs/feature-management-experimentation/40-feature-management/{ => targeting}/target-with-custom-attributes.md (95%)
 rename docs/feature-management-experimentation/40-feature-management/{ => targeting}/target-with-dependencies.md (98%)

diff --git a/docs/feature-management-experimentation/40-feature-management/index.md b/docs/feature-management-experimentation/40-feature-management/index.md
new file mode 100644
index 00000000000..e10667b259d
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/index.md
@@ -0,0 +1,84 @@
+---
+title: Feature Management in Harness FME
+description: Learn about feature management in Harness FME.
+id: index
+slug: /feature-management-experimentation/feature-management
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules
+  - /docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience
+---
+
+## Overview
+
+A feature flag allows you to choose between different code paths in your system at runtime. Feature flags are an integral part of continuous delivery allowing you to decouple deployment from release. They also enable teams to safely merge new features, bug fixes, or other code changes to one central branch in the version control system, which avoids long-lived feature branches and merge issues by integrating code changes frequently.
+
+When you create a feature flag, you specify metadata, including the flag name, description, owners, and tags. This information helps you and your team manage your feature flags and customize them to your team's workflow.
+
+### Feature flags and SEO
+
+Using feature flags to run experiments or deliver different variations of a page can have some impact on SEO, depending on what content is visible to search engines during crawling. Most of the time, the impact is minimal, especially for short-term tests. That said, long-term experiments that serve vastly different versions of the same page might lead to ranking differences if search engines crawl both versions multiple times.
+
+Web crawlers like Google typically perform their initial crawling with JavaScript disabled. This means `getTreatment()` won’t be called, and the bot will receive the control treatment. On the backend, the bot is usually treated as anonymous traffic and should be assigned a new key, just like you would with a real anonymous user.
+
+To avoid SEO issues, it's best practice to maximize the quality of your page before JavaScript rendering—focusing on static elements like titles, text content, and hrefs. Any SEO in place for that initial crawl will remain unaffected by the feature flag unless you introduce targeting rules that behave differently for bots.
+
+### Cloaking considerations
+
+Cloaking refers to deliberately serving different content to search engines than to real users, which can violate search engine guidelines. Feature flags don’t inherently enable cloaking. However, it is possible to cloak content using user agent targeting, such as detecting a search bot and routing it to a specific treatment. But this would require explicit implementation and is not something feature flags do by default.
+
+### Experimentation and SEO
+
+Both Google and Microsoft have confirmed that A/B testing is safe for SEO when done correctly. If you want to provide a consistent experience for crawlers during experimentation, consider using a consistent key for bots during randomization. Just avoid explicitly targeting user agents for different content, which may raise concerns around cloaking.
+
+## Ensuring a consistent user experience
+
+### Random and Deterministic Treatment Assignment
+
+Harness FME (powered by Split) uses a **deterministic hashing algorithm** to decide which treatment (experience) to deliver for a feature flag. The algorithm takes two main inputs:
+
+- **User ID:** This can be an anonymous user ID, logged-in user ID, account number, or any string representing the entity. The ID’s type is specified by its [traffic type](/docs/feature-management-experimentation/management-and-administration/fme-settings/traffic-types/).
+- **Feature flag:** When the SDK initializes, it downloads targeting plans for all flags. Each flag has a unique **seed** — a random number cached as part of the rollout plan and consistent across all SDKs evaluating that flag.
+
+### How Bucketing Works
+
+For example, when `user_1234` calls `getTreatment` on a flag with seed `123456`:
+
+1. The user ID and flag seed are combined and processed through the deterministic hashing algorithm.
+2. The resulting hash is converted to a number and normalized using `Mod 100` into one of 100 buckets.
+3. The user consistently falls into the **same bucket** every time for that feature flag, regardless of which SDK evaluates it.
+
+This means the bucket, not the user, is assigned the treatment.
+
+- If a feature is exposed to 10% of users, users in buckets 0-9 get the feature; others do not.
+- If exposure increases to 30%, users in buckets 0-29 get the feature.
+
+### Independence and Environment-Specific Seeds
+
+- Split **does not retain any prior knowledge** about users.
+- The evaluation happens **every time** a treatment is requested.
+- Each feature flag seed is unique per environment (e.g., staging vs production), so bucketing differs across environments.
+
+### Limit Exposure and Bucketing
+
+Limit exposure controls participation in targeting rules:
+
+- All users are first bucketed by **limit exposure**.
+- For example, with a 50/50 exposure setting:
+  - Buckets 0-49 are subject to targeting and default rules.
+  - Buckets 50-99 receive the default treatment directly, excluding them from the experiment or feature.
+
+### Treatment Order Matters
+
+The order in which treatments are defined can affect the consistency of user experience. [Learn more about treatment order](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#treatment-ordering-and-traffic-distribution).
+
+### Traffic Type and Targeting Granularity
+
+- **User-level targeting:** Use UUIDs (e.g., logged-in user IDs) for consistent user experiences.
+- **Anonymous users:** Use cookies, session IDs, or device IDs, but note that users might not receive consistent experiences if switching devices or clearing cookies.
+- **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/targeting-an-account).
+
+### Important Consideration for Account Targeting
+
+If you do a 50/50 split by account, large accounts with many users might unevenly skew the user count on one side of the experiment, potentially leading to an actual end-user distribution higher than 50%.
+
+Harness FME ensures a consistent user experience by combining deterministic hashing with seed-based bucketing and configurable limit exposure, enabling reliable and predictable feature rollouts across different users and environments.
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
new file mode 100644
index 00000000000..75c955af553
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
@@ -0,0 +1,20 @@
+---
+title: Manage Feature Flags
+description: Explore how to manage your feature flags throughout their lifecycle—scheduling rollouts, controlling treatments, reallocating traffic, using kill switches, and optimizing performances with flag sets.
+sidebar_position: 2
+---
+
+## Overview
+
+This section covers managing feature flags in Harness FME. Learn to schedule rollouts, set alert baselines, use kill switches, reallocate traffic, and leverage flag sets for improved SDK performance. 
+
+Also, explore account-level targeting and how to view and maintain consistent user treatments.
+
+- [Using essential scheduling](/docs/feature-management-experimentation/feature-management/manage-flags/using-essential-scheduling)
+- [Set the alert baseline treatment](/docs/feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment)
+- [Use the kill switch](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch)
+- [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate)
+- [Using flag sets to boost SDK performance](/docs/feature-management-experimentation/feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance)
+- [Targeting an Account or Organization](/docs/feature-management-experimentation/feature-management/manage-flags/targeting-an-account-or-organization)
+- [Viewing User Treatments](/docs/feature-management-experimentation/feature-management/manage-flags/viewing-user-treatments)
+- [Maintaining Consistent Treatments](/docs/feature-management-experimentation/feature-management/manage-flags/maintaining-consistent-treatments)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
similarity index 93%
rename from docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
index 02766d9c400..07f1faa006f 100644
--- a/docs/feature-management-experimentation/40-feature-management/maintaining-consistent-treatments.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
@@ -45,15 +45,15 @@ Example workflow:
 - **Day 1 (Nov 1):**  
   90% get `v1_application`, 10% get `v2_application`.  
   *(Targeting rule with date matcher for Nov 1)*  
-  ![Day 1 targeting rule example](./static/create-date-1.png)
+  ![Day 1 targeting rule example](../static/create-date-1.png)
 
 - **Day 5:**  
   Traffic split adjusted to 80% `v1_application` and 20% `v2_application`.  
-  ![Day 5 targeting rule example](./static/create-date-2.png)
+  ![Day 5 targeting rule example](../static/create-date-2.png)
 
 - **Day 14:**  
   `v1_application` set to 0%, `v2_application` set to 100%.  
-  ![Day 14 targeting rule example](./static/create-date-3.png)
+  ![Day 14 targeting rule example](../static/create-date-3.png)
 
 Existing applications continue to match their original date-based rule and maintain treatment. New applications fall into updated rules.
 
@@ -80,11 +80,11 @@ If you have fewer than ~10k users, you can also:
 - Assign user IDs to the appropriate segment based on stored treatment.
 - Use individual targeting rules with these segments.
 
-![Using segments for treatment targeting](./static/create-date-4.png)
+![Using segments for treatment targeting](../static/create-date-4.png)
 
 Or use segments as part of targeting rules for more flexibility:
 
-![Segment in targeting rule example](./static/create-date-5.png)
+![Segment in targeting rule example](../static/create-date-5.png)
 
 This method requires additional management and is less integrated with Split’s experimentation features. It’s better suited for simple rollouts rather than experiments.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/reallocate-traffic.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
similarity index 92%
rename from docs/feature-management-experimentation/40-feature-management/reallocate-traffic.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
index cc84e7dc563..dc4b231a799 100644
--- a/docs/feature-management-experimentation/40-feature-management/reallocate-traffic.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
@@ -3,6 +3,8 @@ title: Reallocate
 sidebar_label: Reallocate
 description: ""
 sidebar_position: 16
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/reallocate-traffic
 ---
 
 When targeting rules are defined for a feature flag, the rules are used to assign every customer to a treatment. This assignment is permanent until the targeting rules change. This permanence is useful because a customer should not see a different treatment in two back to back calls. 
@@ -22,7 +24,7 @@ To reallocate traffic, do the following:
 
 1. Click the **...menu** icon on the selected feature flag.
 
-   ![](./static/reallocate-traffic.png)
+   ![](../static/reallocate-traffic.png)
 
 2. Click **Reallocate**.
 3. Enter 'REALLOCATE' in the warning alert.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/set-the-alert-baseline-treatment.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/set-the-alert-baseline-treatment.md
similarity index 91%
rename from docs/feature-management-experimentation/40-feature-management/set-the-alert-baseline-treatment.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/set-the-alert-baseline-treatment.md
index 30cd400a6d0..eabc1cb31c6 100644
--- a/docs/feature-management-experimentation/40-feature-management/set-the-alert-baseline-treatment.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/set-the-alert-baseline-treatment.md
@@ -3,14 +3,14 @@ title: Set the alert baseline treatment
 sidebar_label: Set the alert baseline treatment
 description: ""
 sidebar_position: 12
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/set-the-alert-baseline-treatment
 ---
 
-<!-- can we add this paragraph here?
+## Overview
 
 The alert baseline treatment is used if an alert policy has been created. When there is an active alert policy, then the feature flag's alert baseline treatment is compared against all the other treatments when metrics are calculated.
 
--->
-
 :::tip
 Get access to alert policies by contacting your customer success manager or [support@split.io](mailto:support@split.io) and we’ll enable in your account.
 :::
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting-an-account.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/targeting-an-account.md
similarity index 100%
rename from docs/feature-management-experimentation/40-feature-management/targeting-an-account.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/targeting-an-account.md
diff --git a/docs/feature-management-experimentation/40-feature-management/use-the-kill-switch.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/use-the-kill-switch.md
similarity index 92%
rename from docs/feature-management-experimentation/40-feature-management/use-the-kill-switch.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/use-the-kill-switch.md
index a9acd04a362..1f4d0bea207 100644
--- a/docs/feature-management-experimentation/40-feature-management/use-the-kill-switch.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/use-the-kill-switch.md
@@ -3,6 +3,8 @@ title: Use the kill switch
 sidebar_label: Use the kill switch
 description: ""
 sidebar_position: 15
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/use-the-kill-switch
 ---
 
 If a feature flag negatively impacts performance or your user’s experience, you can kill it without changing targeting rules or performing a redeployment. When you kill a feature, all traffic is sent to the default treatment selected for that feature flag.
diff --git a/docs/feature-management-experimentation/40-feature-management/using-essential-scheduling.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-essential-scheduling.md
similarity index 94%
rename from docs/feature-management-experimentation/40-feature-management/using-essential-scheduling.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/using-essential-scheduling.md
index ad9c4d74c53..9265c5bb24c 100644
--- a/docs/feature-management-experimentation/40-feature-management/using-essential-scheduling.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-essential-scheduling.md
@@ -3,6 +3,8 @@ title: Using essential scheduling
 sidebar_label: Using essential scheduling
 description: ""
 sidebar_position: 2
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/using-essential-scheduling
 ---
 
 Scheduling provides the capability to launch a feature on a certain date and time. This allows you to make changes to a feature flag and get approvals ahead of the release date which makes release planning and collaboration easier and more flexible while also increasing the likelihood to release on a target date.
@@ -15,7 +17,7 @@ To use feature flag scheduling, do the following:
 
 1. After you make changes to a feature flag, the **When is this change going into effect** option in the Change summary page appears:
 
-    ![](./static/using-essential-scheduling.png)
+    ![](../static/using-essential-scheduling.png)
 
 2. Select when changes made to the feature flag go into effect as follows:
   * **Immediately.** The feature flag goes into effect immediately. Note: This depends on the environment. If the environment doesn’t require approvals, the change goes live immediately. If approvals are required, the change goes live immediately after it’s approved.
diff --git a/docs/feature-management-experimentation/40-feature-management/using-flag-sets-to-boost-sdk-performance.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
similarity index 95%
rename from docs/feature-management-experimentation/40-feature-management/using-flag-sets-to-boost-sdk-performance.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
index 1fe0293e2ae..eb71cbf4de8 100644
--- a/docs/feature-management-experimentation/40-feature-management/using-flag-sets-to-boost-sdk-performance.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
@@ -3,6 +3,8 @@ title: Using flag sets to boost SDK performance
 sidebar_label: Using flag sets to boost SDK performance
 description: ""
 sidebar_position: 17
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/using-flag-sets-to-boost-sdk-performance
 ---
 
 Flag sets allow you to group FME feature flag definitions that logically belong together. You can configure FME SDK to download a flag set (the subset of your feature flag definitions that you need to evaluate on your client) rather than all of your flags defined in a Harness FME Environment. Flag sets allow you to minimize bandwidth and memory usage, boost app responsiveness, and effectively reduce latency and CPU demands.
@@ -19,7 +21,7 @@ To use flag sets you need to:
 
    For example, given three flags named `experiment_1`, `experiment_2` and `experiment_3`, the flag definition scoped to the Production environment for `experiment_1` can be associated with the flag set `backend`, while the definitions scoped to the Production environment for `experiment_2` and `experiment_3` can be associated with the flag set `frontend`.
 
-    ![](./static/using-flagsets-to-boost-sdk-performance.png)
+    ![](../static/using-flagsets-to-boost-sdk-performance.png)
 
 3. Configure your SDK to filter by flag set
 
diff --git a/docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
similarity index 98%
rename from docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md
rename to docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
index ed2ea627056..c5cf5cc3997 100644
--- a/docs/feature-management-experimentation/40-feature-management/viewing-user-treatments.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
@@ -16,7 +16,7 @@ There are three primary use cases for determining which treatment a user sees:
 
 This question is often asked to validate the rollout plan when a feature flag is changed. You can answer this by going to the **Key page**. Here's an example:
 
-![](./static/use-case-1.png)
+![](../static/use-case-1.png)
 
 In this image, you can see that the user will see the **'on'** treatment for the `admin_panel_access` flag every time they log in, based on the current rollout plan.
 
@@ -37,7 +37,7 @@ This question is common when debugging live customer experiences or when making
 
 You can answer this by going to **Data hub**. Example:
 
-![](./static/use-case-2.png)
+![](../static/use-case-2.png)
 
 Here, you search for a specific key (e.g., user ID). Split fetches the live treatments currently assigned.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/customer-dashboard.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
similarity index 89%
rename from docs/feature-management-experimentation/40-feature-management/customer-dashboard.md
rename to docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
index bd47e9e05b7..451f3c325cb 100644
--- a/docs/feature-management-experimentation/40-feature-management/customer-dashboard.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
@@ -3,6 +3,8 @@ title: Review customer dashboard
 sidebar_label: Review customer dashboard
 description: ""
 sidebar_position: 18
+redirect_from: 
+  - /docs/feature-management-experimentation/feature-management/customer-dashboard
 ---
 
 The customer dashboard provides feature flag, segment, and attribute information on a particular user key. This dashboard can be navigated to by clicking on a unique key when viewing impression data or using the search bar on the left navigation panel.
@@ -11,7 +13,7 @@ The customer dashboard provides feature flag, segment, and attribute information
 
 The first tab shows the treatments that are served to that customer for each feature flag in your project. Note that attributes of a customer key remain private to our SDKs, feature flags which use attributes for targeting do not show an expected treatment. 
 
-![](./static/traffic-key-dashboard.png)
+![](../static/traffic-key-dashboard.png)
 
 ## Segments 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/export-data.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/export-data.md
similarity index 97%
rename from docs/feature-management-experimentation/40-feature-management/export-data.md
rename to docs/feature-management-experimentation/40-feature-management/monitoring-analysis/export-data.md
index 4666446e657..f2c900053f8 100644
--- a/docs/feature-management-experimentation/40-feature-management/export-data.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/export-data.md
@@ -1,10 +1,10 @@
 ---
 title: Export data
 sidebar_label: Export data
-helpdocs_is_private: false
-helpdocs_is_published: true
 description: ""
 sidebar_position: 20
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/export-data
 ---
 
 The data export functionality within the Data hub gives development teams the ability to query and export all impression and event data. With data export, development teams can:
diff --git a/docs/feature-management-experimentation/40-feature-management/impressions.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
similarity index 97%
rename from docs/feature-management-experimentation/40-feature-management/impressions.md
rename to docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
index bf20be52946..5e010b0aafe 100644
--- a/docs/feature-management-experimentation/40-feature-management/impressions.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
@@ -5,6 +5,7 @@ description: ""
 sidebar_position: 18
 redirect_from:
 - /docs/feature-management-experimentation/feature-management/faqs/is-the-feature-flag-impression-toggle-supported
+- /docs/feature-management-experimentation/feature-management/impressions
 ---
 
 import Tabs from '@theme/Tabs';
@@ -61,23 +62,23 @@ To turn impression tracking on or off:
 
 1. Click the ellipses menu on a feature flag Definition tab, and select the **Track impressions** menu item.
 
-   ![](./static/impressions-track-impressions-menu.png)
+   ![](../static/impressions-track-impressions-menu.png)
 
 2. Click the **Impression tracking** toggle and click **Save**.
 
-   ![](./static/impressions-toggle.png)
+   ![](../static/impressions-toggle.png)
 
 3. On the feature flag Definition tab, click **Review changes** and click **Save**.
 
 When impression tracking is off, you will see "Impression tracking off" at the top right of the feature flag definition page. The arrows will also be grayed out (shown with tooltip) on the the feature flag definition card.
 
-![](static/impressions-tracking-visual-cues-1.png)
+![](../static/impressions-tracking-visual-cues-1.png)
 
 ### Supported SDKs and customer-deployed components for impression tracking
 
 The impression tracking toggle only works with supported FME SDKs and customer-deployed components. If the SDK you are using does not support the toggle, impressions will still be sent to Harness servers—even if impression tracking is turned off for the feature flag definition.
 
-![](./static/impressions-tracking-visual-cues-1.png)
+![](../static/impressions-tracking-visual-cues-1.png)
 
 :::info
 If you are using an older SDK or customer-deployed component version that does not support the toggle, Harness recommend upgrading to a supported version to ensure that impression tracking behavior aligns with your feature flag settings.
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/index.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/index.md
new file mode 100644
index 00000000000..74e93778db3
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/index.md
@@ -0,0 +1,14 @@
+---
+title: Monitoring & Analysis
+description: Understand how to monitor your feature flags in real time with dashboards, impressions, traffic insights, Live Tail, and exporting data for comprehensive analysis and decision-making.
+sidebar_position: 4
+---
+
+## Overview
+
+Track and analyze your feature flags and experiments. This section covers how to review customer dashboards, analyze impressions, monitor live traffic, and export data for further analysis.
+
+- [Review customer dashboard](/docs/feature-management-experimentation/feature-management/monitoring-analysis/customer-dashboard)
+- [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions)
+- [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail)
+- [Export data](/docs/feature-management-experimentation/feature-management/monitoring-analysis/export-data)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/live-tail.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
similarity index 97%
rename from docs/feature-management-experimentation/40-feature-management/live-tail.md
rename to docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
index 46a4c21f70e..480c9ac98ff 100644
--- a/docs/feature-management-experimentation/40-feature-management/live-tail.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
@@ -3,6 +3,8 @@ title: Live tail
 sidebar_label: Live tail
 description: ""
 sidebar_position: 19
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/live-tail
 ---
 
 The Live tail functionality within Data hub gives development teams a single place to view and query all impression and event data. With Live tail, development teams can:
@@ -13,7 +15,7 @@ The Live tail functionality within Data hub gives development teams a single pla
 
 This feature allows teams, for example, to check if a feature flag is being evaluated properly or events are being received from data integrations.
 
-![](./static/live-tail.png)
+![](../static/live-tail.png)
 
 ## Query impressions 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/set-the-default-treatment.md b/docs/feature-management-experimentation/40-feature-management/set-the-default-treatment.md
deleted file mode 100644
index 322b9706034..00000000000
--- a/docs/feature-management-experimentation/40-feature-management/set-the-default-treatment.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-title: Set the default treatment
-sidebar_label: Set the default treatment
-description: ""
-sidebar_position: 11
----
-
-If a feature flag is killed or the customer is not exposed to any targeting rules, the flag serves a treatment selected by you. This is also known as the default treatment. Your treatment should always be one that exposes fully tested and safe code. In an on/off feature flag, the default treatment is typically set to off. In a multivariate feature, the default might be off, or it might be defined as the treatment that is currently used by 100% of traffic. To set this treatment, refer to the [Target customers](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) guide.
-
-<!-- future proposed rewrite, combining two articles (https://help.split.io/hc/en-us/articles/360020528192-Default-treatment, https://help.split.io/hc/en-us/articles/360020527672-Set-the-default-treatment) :
-
-The default treatment is returned by the SDK in the following scenarios:
-
-* **The end user does not meet any defined conditions:** The default treatment is shown to end users who do not meet any of the conditions in the targeting rules.
-
-* **The flag is killed:** If a particular feature flag is killed, the default treatment overrides the existing targeting rules and is returned for **all** customers.
-
-Therefore, if a feature flag is killed or the customer is not exposed to any targeting rules, the flag serves a treatment selected by you. This is also known as the default treatment.
-
-:::tip[Tip]
-While you can set any of the treatments in your targeting rules as the default,
-your default treatment should always be one that exposes fully tested and safe code.
-* In an **on/off** feature flag, the default treatment is typically set to **'off'**.
-* In a multivariant feature flag, the default might be **'off'**, or it might be defined as the treatment that is currently used by 100% of traffic.
-* At the start of a percentage rollout, we recommend choosing the safest treatment for your customers as the default treatment (that is, **'off'** or **'old version'**).
-* At the end of a percentage rollout (at 100% rolled out), you may want to make the new experience the default in case the feature is accidentally killed before being removed.
-
-To set this treatment, refer to the [Target customers](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) guide.
-:::
-
--->
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/control-treatment.md b/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
similarity index 96%
rename from docs/feature-management-experimentation/40-feature-management/control-treatment.md
rename to docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
index 6e9d83b9968..2557092bb22 100644
--- a/docs/feature-management-experimentation/40-feature-management/control-treatment.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
@@ -3,6 +3,8 @@ title: Control treatment
 sidebar_label: Control treatment
 description: ""
 sidebar_position: 13
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/control-treatment
 ---
 
 The **control** treatment is a reserved treatment in FME. You cannot create a treatment named **control**. The **control** treatment is returned by the SDK in the following scenarios:
diff --git a/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
similarity index 55%
rename from docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md
rename to docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
index 3497a3c1d22..9da09ee8777 100644
--- a/docs/feature-management-experimentation/40-feature-management/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
@@ -1,44 +1,22 @@
 ---
-title: Feature Management in Harness FME
-sidebar_label: Overview
-description: Learn about feature management in Harness FME.
+title: Create a Feature Flag
+sidebar_label: Create a Feature Flag
+description: Learn how to create a feature flag in Harness FME.
 sidebar_position: 1
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules
-  - /docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience
+  - /docs/feature-management-experimentation/feature-management/create-a-feature-flag
 ---
 
 ## Overview
 
-A feature flag allows you to choose between different code paths in your system at runtime. Feature flags are an integral part of continuous delivery allowing you to decouple deployment from release. They also enable teams to safely merge new features, bug fixes, or other code changes to one central branch in the version control system, which avoids long-lived feature branches and merge issues by integrating code changes frequently.
-
-When you create a feature flag, you specify metadata, including the flag name, description, owners, and tags. This information helps you and your team manage your feature flags and customize them to your team's workflow.
-
-### Feature flags and SEO
-
-Using feature flags to run experiments or deliver different variations of a page can have some impact on SEO, depending on what content is visible to search engines during crawling. Most of the time, the impact is minimal, especially for short-term tests. That said, long-term experiments that serve vastly different versions of the same page might lead to ranking differences if search engines crawl both versions multiple times.
-
-Web crawlers like Google typically perform their initial crawling with JavaScript disabled. This means `getTreatment()` won’t be called, and the bot will receive the control treatment. On the backend, the bot is usually treated as anonymous traffic and should be assigned a new key, just like you would with a real anonymous user.
-
-To avoid SEO issues, it's best practice to maximize the quality of your page before JavaScript rendering—focusing on static elements like titles, text content, and hrefs. Any SEO in place for that initial crawl will remain unaffected by the feature flag unless you introduce targeting rules that behave differently for bots.
-
-### Cloaking considerations
-
-Cloaking refers to deliberately serving different content to search engines than to real users, which can violate search engine guidelines. Feature flags don’t inherently enable cloaking. However, it is possible to cloak content using user agent targeting, such as detecting a search bot and routing it to a specific treatment. But this would require explicit implementation and is not something feature flags do by default.
-
-### Experimentation and SEO
-
-Both Google and Microsoft have confirmed that A/B testing is safe for SEO when done correctly. If you want to provide a consistent experience for crawlers during experimentation, consider using a consistent key for bots during randomization. Just avoid explicitly targeting user agents for different content, which may raise concerns around cloaking.
-
-## Create a feature flag
-
 To create a feature flag, do the following:
 
 1. From the left navigation, click **Feature flags**. The feature flag list view appears.
 
 2. Click the **Create feature flag** button to create a new feature flag.
 
-    ![](./static/create-a-feature-flag.png)
+    ![](../static/create-a-feature-flag.png)
 
 3. Enter the following information for your new feature flag to help you and your team manage your feature flags and customize them to your team's workflow:
 
@@ -55,63 +33,10 @@ To create a feature flag, do the following:
 
 4. Click the **Create feature flag** button to create your feature flag. Your new flag is displayed. 
 
-    ![](./static/create-a-feature-flag-initiate-environment.png)
+    ![](../static/create-a-feature-flag-initiate-environment.png)
 
 1. To configure your feature flag for a particular environment, select the environment on the left panel, and click the Initiative environment button within the Definition tab. 
 
-## Ensuring a consistent user experience
-
-### Random and Deterministic Treatment Assignment
-
-Harness FME (powered by Split) uses a **deterministic hashing algorithm** to decide which treatment (experience) to deliver for a feature flag. The algorithm takes two main inputs:
-
-- **User ID:** This can be an anonymous user ID, logged-in user ID, account number, or any string representing the entity. The ID’s type is specified by its [traffic type](/docs/feature-management-experimentation/management-and-administration/fme-settings/traffic-types/).
-- **Feature flag:** When the SDK initializes, it downloads targeting plans for all flags. Each flag has a unique **seed** — a random number cached as part of the rollout plan and consistent across all SDKs evaluating that flag.
-
-### How Bucketing Works
-
-For example, when `user_1234` calls `getTreatment` on a flag with seed `123456`:
-
-1. The user ID and flag seed are combined and processed through the deterministic hashing algorithm.
-2. The resulting hash is converted to a number and normalized using `Mod 100` into one of 100 buckets.
-3. The user consistently falls into the **same bucket** every time for that feature flag, regardless of which SDK evaluates it.
-
-This means the bucket, not the user, is assigned the treatment.
-
-- If a feature is exposed to 10% of users, users in buckets 0-9 get the feature; others do not.
-- If exposure increases to 30%, users in buckets 0-29 get the feature.
-
-### Independence and Environment-Specific Seeds
-
-- Split **does not retain any prior knowledge** about users.
-- The evaluation happens **every time** a treatment is requested.
-- Each feature flag seed is unique per environment (e.g., staging vs production), so bucketing differs across environments.
-
-### Limit Exposure and Bucketing
-
-Limit exposure controls participation in targeting rules:
-
-- All users are first bucketed by **limit exposure**.
-- For example, with a 50/50 exposure setting:
-  - Buckets 0-49 are subject to targeting and default rules.
-  - Buckets 50-99 receive the default treatment directly, excluding them from the experiment or feature.
-
-### Treatment Order Matters
-
-The order in which treatments are defined can affect the consistency of user experience. [Learn more about treatment order](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#treatment-ordering-and-traffic-distribution).
-
-### Traffic Type and Targeting Granularity
-
-- **User-level targeting:** Use UUIDs (e.g., logged-in user IDs) for consistent user experiences.
-- **Anonymous users:** Use cookies, session IDs, or device IDs, but note that users might not receive consistent experiences if switching devices or clearing cookies.
-- **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/targeting-an-account).
-
-### Important Consideration for Account Targeting
-
-If you do a 50/50 split by account, large accounts with many users might unevenly skew the user count on one side of the experiment, potentially leading to an actual end-user distribution higher than 50%.
-
-Harness FME ensures a consistent user experience by combining deterministic hashing with seed-based bucketing and configurable limit exposure, enabling reliable and predictable feature rollouts across different users and environments.
-
 ## Create a rollout plan
 
 After you have created your feature flag, you can [define treatments and set targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) for your feature flag. This is also known as creating a rollout plan.
@@ -154,7 +79,7 @@ To avoid this issue, your workspace administrator might consider one of the foll
 
 When trying to delete a feature flag that exists in multiple Environments, an error message appears preventing deletion—even after deleting all the custom targeting rules.
 
-![](./static/delete-error.png)
+![](../static/delete-error.png)
 
 The error refers to all feature flag definitions within each environment, including Treatments, Individual targets, Dynamic Configurations, Default Rules, and Custom Targeting Rules.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/create-a-rollout-plan.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
similarity index 99%
rename from docs/feature-management-experimentation/40-feature-management/create-a-rollout-plan.md
rename to docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
index 79393885ce1..0570f7e17d2 100644
--- a/docs/feature-management-experimentation/40-feature-management/create-a-rollout-plan.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
@@ -2,6 +2,8 @@
 title: Create a rollout plan
 description: Learn how to create a rollout plan in Harness FME.
 sidebar_position: 3
+redirect_from:
+  - /docs/feature-management-experimentation/feature-management/create-a-rollout-plan
 ---
 
 ## Overview
@@ -100,7 +102,7 @@ To edit your configuration as key-value pairs, do the following:
 1. Enter values for each of your keys (e.g., green, size 16, Buy now!, /promo_images/svg/kermit.svg).
 1. Click the **Add new pair** button to add another pair or click the **Minus** icon to remove a pair for each treatment. You can attach as many key-value pairs as necessary for each of your treatments.
 
-   ![](./static/rollout-1.png)
+   ![](../static/rollout-1.png)
 
 :::tip
 All keys and values are interpreted as strings when configurations are saved in this format (e.g., 5 is stringified and sent as 5) so you need to convert your code from that format at runtime.
@@ -113,7 +115,7 @@ To edit your configuration using the JSON form, do the following:
 1. From the left navigation, click **Feature flags** and then your desired feature flag.
 1. In the **Format** field of the **Dynamic configuration** area, select **JSON** as your format. You get a JSON editor for each of your treatments.
 
-   ![](./static/rollout-2.png)
+   ![](../static/rollout-2.png)
 
 1. Insert valid JSON into the editor. Basic JSON linting is done. Nested JSON objects are permitted.
 1. Click **expand or collapse** if you have a large JSON object inserted as a configuration and you may need to expand or collapse it when editing.
@@ -138,7 +140,7 @@ After you have defined your treatments, the rest of the Definition tab controls
 
 Evaluation order mirrors the order that the options appear on the Definition tab. Once a user matches one of the below sections, no further evaluation is performed for that user. When a flag is killed, the default treatment is served in all cases until the flag is made active again through a restore or update.
 
-![](./static/rollout-3.png)
+![](../static/rollout-3.png)
 
 Targeting is evaluated as follows:
 
diff --git a/docs/feature-management-experimentation/40-feature-management/default-treatment.md b/docs/feature-management-experimentation/40-feature-management/setup/default-treatment.md
similarity index 93%
rename from docs/feature-management-experimentation/40-feature-management/default-treatment.md
rename to docs/feature-management-experimentation/40-feature-management/setup/default-treatment.md
index e0f7cdaadee..24a579b5eed 100644
--- a/docs/feature-management-experimentation/40-feature-management/default-treatment.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/default-treatment.md
@@ -1,10 +1,11 @@
 ---
-title: Default treatment
-sidebar_label: Default treatment
+title: Set the default treatment
+sidebar_label: Set the default treatment
 description: "Learn about the default treatment in Harness FME."
 sidebar_position: 14
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/faqs/what-is-the-difference-between-default-rule-and-default-treatment
+  - /docs/feature-management-experimentation/feature-management/default-treatment
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
similarity index 96%
rename from docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md
rename to docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
index 9ec3944318f..195183977d2 100644
--- a/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
@@ -6,6 +6,7 @@ sidebar_position: 3
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/best-practices/why-setting-the-order-of-treatments-matters
   - /docs/feature-management-experimentation/feature-management/faqs/how-can-i-override-individually-targeted-users-with-a-superseding-targeting-rule
+  - /docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting
 ---
 
 ## Define feature flag treatments and targeting
@@ -28,7 +29,7 @@ Initiating your feature flag definition for a given environment will create an i
 
 If a feature flag definition is not initiated for an environment, the following page appears:
 
-![](./static/create-a-feature-flag-initiate-environment.png)
+![](../static/create-a-feature-flag-initiate-environment.png)
 
 To initiate your feature flag definition for an environment, do one of the following:
 
@@ -37,7 +38,7 @@ To initiate your feature flag definition for an environment, do one of the follo
 
 Once the feature flag definition exists for a given environment, the feature flag definition tab will show treatments and targeting rules similar to the following:
 
-![](./static/define-feature-flag-treatments-and-targeting-main-view.png)
+![](../static/define-feature-flag-treatments-and-targeting-main-view.png)
 
 You will be able to modify the feature flag treatments, dynamic configurations, and targeting rules before saving. This allows you to configure your feature flag for a given environment.
 
@@ -83,7 +84,7 @@ In the Individual targets area, you can:
 
 Click the **Add new individual target** button to assign a selected treatment to individual users or segments (e.g., a QA team, internal team, or a limited beta), as shown below.
 
-![](./static/define-feature-flag-treatments-and-targeting-individual.png)
+![](../static/define-feature-flag-treatments-and-targeting-individual.png)
 
 ### Targeting rules
 
@@ -101,7 +102,7 @@ In the Targeting rules area, you can:
 
     * Optionally select **Distribute treatments as follows** and define a ___percentage distribution___ to [randomly distribute](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience) users and segments between your feature flag treatments (variations) based on the percentages you decide. This is also called a _percentage rollout_, as shown below.
 
-   ![](./static/define-feature-flag-treatments-and-targeting-attribute-based.png)
+   ![](../static/define-feature-flag-treatments-and-targeting-attribute-based.png)
 
 :::tip[Tip: Distribute treatments by percentage to enable experimentation]
 If you have at least one targeting rule with ___percentage distribution___, then you can ___compare metric results between feature flag treatments___ (on the [Metrics impact tab](/docs/feature-management-experimentation/experimentation/experiment-results/viewing-experiment-results/).
@@ -111,7 +112,7 @@ If you have at least one targeting rule with ___percentage distribution___, then
 
 Targeting rules are evaluated from top to bottom, meaning that the higher a rule appears, the higher its priority. You can override individually targeted users by placing a more specific rule above the one that includes them.
 
-![](./static/rules-1.png)
+![](../static/rules-1.png)
 
 For example, if you have a rule that individually targets users by ID or segment, but you need to give certain users a different treatment based on an attribute (such as slow network speed), create a higher-priority rule for that attribute.
 
@@ -121,7 +122,7 @@ In the **Set targeting rules** section:
 1. Specify the desired treatment (e.g., _Off_).
 1. In the **Else if** part of the rule, include your original segment or user IDs.
 
-![](./static/rules-2.png)
+![](../static/rules-2.png)
 
 This setup ensures that the overriding condition is applied before the individual targeting rule.
 
@@ -147,7 +148,7 @@ You can implement a feature toggle that switches between `on` and `off` automati
    * You can turn the flag `on` or `off` at a specific time, for a subset of users or for all users.
    * You can also configure it for a time window. For example, if you want to turn a message on alerting users during a maintenance window, you can set up the rule as shown in the following screenshot:
 
-   ![](./static/targeting-rule-pass-system-time.png)
+   ![](../static/targeting-rule-pass-system-time.png)
 
 
 ## Setting the alert baseline treatment
@@ -171,7 +172,7 @@ Feature flag rules are evaluated in the following order:
 
 Individual targets are evaluated in order. For example, if **Bob** is a user ID in the **Internal_QA** segment, then **Bob** will get **on** even though you’ve specifically assigned that key **off**.
 
-![](./static/feature-flag-rules-evaluation-order-example.png)
+![](../static/feature-flag-rules-evaluation-order-example.png)
 
 ### Treatment ordering and traffic distribution
 
diff --git a/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md b/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
similarity index 97%
rename from docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md
rename to docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
index a1778f87f55..b68f0a3ce90 100644
--- a/docs/feature-management-experimentation/40-feature-management/dynamic-configurations.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
@@ -5,6 +5,7 @@ description: "Learn how to use Dynamic Configuration in feature flags, enabling
 sidebar_position: 5
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/faqs/dynamic-configuration-section-does-not-show-up/
+  - /docs/feature-management-experimentation/feature-management/dynamic-configurations
 ---
 
 ## Overview
@@ -38,7 +39,7 @@ To set up dynamic configuration, do the following:
 If you select **Key-value pairs** as your format, attach one or more configurations to each of your treatments as follows:
 
 
-![](./static/dynamic-configurations-key-value-pairs.png)
+![](../static/dynamic-configurations-key-value-pairs.png)
 
 1. In the Enter a key field, enter the keys that you want your code to check for (e.g., color, font size, message copy). You can add more keys as desired.
 2. In the Enter a value field,  enter the values for each of your keys. (e.g., green, size 16, Buy now!).
@@ -52,7 +53,7 @@ If you select **Key-value pairs** as your format, attach one or more configurati
 
 Select JSON as your format to get a JSON editor for each of your treatments as follows:
 
-![](./static/dynamic-configurations-json.png)
+![](../static/dynamic-configurations-json.png)
 
 1. In the JSON editor, enter valid JSON into the editor. Basic JSONlinting is done and nested JSON objects are permitted.
 2. Click to expand or collapse when editing if you have a large JSON object entered as a configuration.
diff --git a/docs/feature-management-experimentation/40-feature-management/edit-treatments.md b/docs/feature-management-experimentation/40-feature-management/setup/edit-treatments.md
similarity index 84%
rename from docs/feature-management-experimentation/40-feature-management/edit-treatments.md
rename to docs/feature-management-experimentation/40-feature-management/setup/edit-treatments.md
index cebe75dbf6f..5d15f87c847 100644
--- a/docs/feature-management-experimentation/40-feature-management/edit-treatments.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/edit-treatments.md
@@ -3,11 +3,13 @@ title: Edit treatments
 sidebar_label: Edit treatments
 description: ""
 sidebar_position: 4
+redirect_from:
+- /docs/feature-management-experimentation/feature-management/edit-treatments
 ---
 
 Every feature you want to release in FME has at least two treatments. In the simplest case, these are **on** and **off**. You can edit the treatment's name and description as well as add additional treatments at any time. 
 
-![](./static/edit-treatments.png)
+![](../static/edit-treatments.png)
 
 :::note
 You can only create a maximum of 20 treaments.
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/index.md b/docs/feature-management-experimentation/40-feature-management/setup/index.md
new file mode 100644
index 00000000000..506368d3403
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/setup/index.md
@@ -0,0 +1,18 @@
+---
+title: Feature Management Setup
+description: Get started with creating feature flags, defining treatments, setting default and control treatments, creating rollout plans, and leveraging dynamic configuration to tailor your feature management setup.
+sidebar_position: 1
+sidebar_label: Setup
+---
+
+## Overview
+
+Learn how to create and configure feature flags in Harness FME. This section guides you through creating feature flags, defining treatments, setting defaults and controls, planning rollouts, and applying dynamic configurations.
+
+- [Create a Feature Flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag)
+- [Create a rollout plan](/docs/feature-management-experimentation/feature-management/setup/create-a-rollout-plan)
+- [Define feature flag treatments and targeting](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting)
+- [Edit treatments](/docs/feature-management-experimentation/feature-management/setup/edit-treatments)
+- [Dynamic configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations)
+- [Set the default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment)
+- [Control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/index.md b/docs/feature-management-experimentation/40-feature-management/targeting/index.md
new file mode 100644
index 00000000000..d917ce574e7
--- /dev/null
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/index.md
@@ -0,0 +1,15 @@
+---
+title: Targeting
+description: Master targeting strategies by using custom attributes, segments, dependencies, and exposure limits to precisely control which users see which features and when.
+sidebar_position: 5
+---
+
+## Overview
+
+Control which users see your features and when. This section explains how to create and target segments, limit exposure, apply custom attributes, and manage dependencies for fine-grained feature rollout control.
+
+- [Create a segment](/docs/feature-management-experimentation/feature-management/targeting/segments)
+- [Target segments](/docs/feature-management-experimentation/feature-management/targeting/target-segments)
+- [Limiting exposure](/docs/feature-management-experimentation/feature-management/targeting/limiting-exposure)
+- [Target with custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes)
+- [Target with dependencies](/docs/feature-management-experimentation/feature-management/targeting/target-with-dependencies)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/limiting-exposure.md b/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
similarity index 97%
rename from docs/feature-management-experimentation/40-feature-management/limiting-exposure.md
rename to docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
index 0cade5d9e3f..4a665229360 100644
--- a/docs/feature-management-experimentation/40-feature-management/limiting-exposure.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
@@ -12,7 +12,7 @@ To use this feature, do the following:
 1. In your selected feature flag, go to the Targeting rules area.
 2. Click the **Limit exposure** button.
 
-    ![](./static/limiting-exposure.png)
+    ![](../static/limiting-exposure.png)
 
 3. In the Limit exposure field, enter the percentage of traffic that you want to go through the targeting rules. 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/segments.md
similarity index 94%
rename from docs/feature-management-experimentation/40-feature-management/segments.md
rename to docs/feature-management-experimentation/40-feature-management/targeting/segments.md
index df8df084819..b7f49dc0b1d 100644
--- a/docs/feature-management-experimentation/40-feature-management/segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/segments.md
@@ -55,11 +55,11 @@ This article shows how to work with Standard, Large, and Rule-based segments and
 
 The segment type is displayed at the top of each Segment page in Harness FME, as shown below.
 
-<div style={{maxWidth:600}}> ![](./static/create-a-segment-segment-type.png) </div>
+<div style={{maxWidth:600}}> ![](../static/create-a-segment-segment-type.png) </div>
 
 In Harness FME, you can filter the segments listed on the Segments pane by segment type.
 
-<div style={{maxWidth:400}}> ![](./static/create-a-segment-filter-1.png) </div>
+<div style={{maxWidth:400}}> ![](../static/create-a-segment-filter-1.png) </div>
 
 ## Creating a segment
 
@@ -69,7 +69,7 @@ To create a segment:
 
 1. From the navigation bar, click **Create segment**. The Create a segment page appears.
 
-   <div style={{maxWidth:700}}> ![](./static/create-a-segment-page-1.png) </div>
+   <div style={{maxWidth:700}}> ![](../static/create-a-segment-page-1.png) </div>
     
 2. Fill in the fields as follows:
   * **Name.** Give your segment an easily recognizable name.
@@ -93,7 +93,7 @@ You need to add a segment definition for a given environment (this creates an em
 
 1. Select a segment in the Segments pane to open the segment page.
 
-   ![](./static/create-a-segment-add-definition.png)
+   ![](../static/create-a-segment-add-definition.png)
 
 2. In the Environment dropdown, select a Harness FME environment where the segment will be defined.
 
@@ -101,7 +101,7 @@ You need to add a segment definition for a given environment (this creates an em
 
 4. In the User section, click **Add user**.
 
-   ![](./static/create-a-segment-add-user.png)
+   ![](../static/create-a-segment-add-user.png)
 
     ___For Standard segments___ you can choose to individually add user IDs or to add them via file import.
 
@@ -123,11 +123,11 @@ To import user IDs into a Large segment you need to prepare a [CSV import file](
 
 1. On the segment page, click **Add user** and select **Import via API**.
 
-   ![](./static/create-a-segment-import-via-api.png)
+   ![](../static/create-a-segment-import-via-api.png)
 
 2. On the Change summary page, click **Save** to generate an upload URL.
 
-   ![](./static/create-a-segment-upload-url.png)
+   ![](../static/create-a-segment-upload-url.png)
 
     The upload URL will time out after 5 minutes, and the Large segment change request will be automatically withdrawn if the upload URL was not used.
 
@@ -149,11 +149,11 @@ Replace `filename.csv` with the name of your CSV file.
 
 4. Run the curl command from the location of your CSV file. You may need to wait a moment for the command to complete. During this time, the segment will show pending changes in Harness.
 
-   ![](./static/create-a-segment-pending.png)
+   ![](../static/create-a-segment-pending.png)
 
 5. Refresh the Segment page to see the segment size updated. You can search for a user ID to confirm that it is in the Large segment.
 
-   ![](./static/create-a-segment-search.png)
+   ![](../static/create-a-segment-search.png)
 
 ### Append user IDs to a Large segment
 
@@ -176,19 +176,19 @@ Follow these steps to delete all keys from the segment and delete the segment:
 1. Locate the down arrow on top of the checkboxes column.
 1. Click on it to display two menu items.
    
-   ![](./static/delete-1.png)
+   ![](../static/delete-1.png)
 
 1. Select all records from there. The **Delete** button appears.
 
-   ![](./static/delete-2.png)
+   ![](../static/delete-2.png)
 
 1. Once the keys are deleted, delete the segment definition by clicking on the Ellipses icon in the **Definition** section and selecting **Delete definition**.
    
-   ![](./static/delete-3.png)
+   ![](../static/delete-3.png)
 
 1. Click on the Ellipses icon above **Owners** and select **Delete**.
    
-   ![](./static/delete-4.png)
+   ![](../static/delete-4.png)
 
 ## CSV import file format
 
diff --git a/docs/feature-management-experimentation/40-feature-management/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
similarity index 90%
rename from docs/feature-management-experimentation/40-feature-management/target-segments.md
rename to docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index d87a564edd0..f73e0e7430a 100644
--- a/docs/feature-management-experimentation/40-feature-management/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -21,20 +21,20 @@ Server-side SDKs do not yet support Large segments, but soon will. Until they ar
 
 You can target segments in individual targeting rules. These rules assign a treatment to the segment. In the following example, the feature flag will serve **on** to all user IDs in the given segments.
 
-![](./static/target-segments-individual-targets.png)
+![](../static/target-segments-individual-targets.png)
 
 After you select a segment, you can see the segment type indicated by the input pill icon in the To segments field.
 
-![](./static/target-segments-to-segments-input.png)
+![](../static/target-segments-to-segments-input.png)
 
 ## Attribute based targeting rules
 
 You can also target Standard, Large, and Rule-based segments in attribute based targeting rules. The following example is equivalent to the individual targeting rule shown above.
 
-![](./static/target-segments-attribute-based-targeting.png)
+![](../static/target-segments-attribute-based-targeting.png)
 
 You can also use ___percentage distribution___ to randomly distribute treatments among the user IDs in a segment, as shown below.
 
-![](./static/target-segments-percentage-distribution.png)
+![](../static/target-segments-percentage-distribution.png)
 
 See [Targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#targeting-rules) for more information on feature flag targeting, percentage distribution, and rules' evaluation order.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
similarity index 95%
rename from docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md
rename to docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
index a3399e6e5e3..f476579b45f 100644
--- a/docs/feature-management-experimentation/40-feature-management/target-with-custom-attributes.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
@@ -76,13 +76,13 @@ To create a custom attribute that will appear as a User Attribute in your featur
 7. In the Description field, optionally enter a description of the attribute.
 8. In the Type dropdown, select an attribute value type (e.g., String to represent text values, Boolean to represent true or false values, etc.).
 
-   ![](./static/target-with-custom-attributes-ios-version.png)
+   ![](../static/target-with-custom-attributes-ios-version.png)
 
    :::tip
    When you select the Semver or String attribute type, you can create a list of suggested values to match against. These suggested values will appear in Split UI when you are creating your feature flag attribute based targeting rules.
    :::
 
-   ![](./static/target-with-custom-attributes-semver-suggested.png)
+   ![](../static/target-with-custom-attributes-semver-suggested.png)
 
 9. Click the **Create** button. A new custom attribute is created and displayed on the Attributes page.
 
@@ -146,45 +146,45 @@ After you [create a feature flag](/docs/feature-management-experimentation/featu
 
 1. In Harness FME, on the Definition tab of a feature flag, in the Targeting rules area, click the **Add attribute based targeting rules** button. The **IF** field/dropdown menu appears.
 
-   ![](./static/target-with-custom-attributes-using-custom-attributes-01.png)
+   ![](../static/target-with-custom-attributes-using-custom-attributes-01.png)
 
 2. Choose a **User Attribute** (custom attribute):
   Choose a custom attribute from the **IF** dropdown menu’s **User Attributes** section.
 
-   ![](./static/target-with-custom-attributes-using-custom-attributes-02-new-attribute.png)
+   ![](../static/target-with-custom-attributes-using-custom-attributes-02-new-attribute.png)
     
     Or **create a new custom attribute**:
   You can also directly click within the **IF** field, type a new custom attribute ID, and click **New attribute “your new ID”** to create a new custom attribute.
 
-   ![](./static/target-with-custom-attributes-using-custom-attributes-02-user-attribute.png)
+   ![](../static/target-with-custom-attributes-using-custom-attributes-02-user-attribute.png)
 
 3. Select a matcher to evaluate the attribute values passed in from your source code. For more information about matchers and how they evaluate values, see the [Custom attribute types and matchers](#custom-attribute-types-and-matchers) section below.
 
-   ![](./static/target-with-custom-attributes-using-custom-attributes-03.png)
+   ![](../static/target-with-custom-attributes-using-custom-attributes-03.png)
 
 You can also directly click within the IF field, type a new custom attribute ID, and click New attribute “your new ID” to create a new custom attribute.
 
 4. Complete the targeting rule by filling in the values to match against and choosing the treatment(s) to serve.
 
-    ![](./static/target-with-custom-attributes-using-custom-attributes-04.png)
+    ![](../static/target-with-custom-attributes-using-custom-attributes-04.png)
 
    Additional examples:
 
     Serve the `on` treatment for users with custom attribute `app_version` greater than or equal to 16.0.0:
 
-    ![](./static/target-with-custom-attributes-using-custom-attributes-04-app-version.png)
+    ![](../static/target-with-custom-attributes-using-custom-attributes-04-app-version.png)
 
     Serve the `on` treatment for users with custom attribute `age` greater than or equal to 20:
 
-    ![](./static/target-with-custom-attributes-using-custom-attributes-04-age.png)
+    ![](../static/target-with-custom-attributes-using-custom-attributes-04-age.png)
  
     Serve the `on` treatment for users with custom attribute `deal_size` between 500,000 and 10,000,000:
 
-    ![](./static/target-with-custom-attributes-using-custom-attributes-04-deal-size.png)
+    ![](../static/target-with-custom-attributes-using-custom-attributes-04-deal-size.png)
   
     Serve the `on` treatment for users with custom attribute `registered_date` on or after a specified date:
 
-    ![](./static/target-with-custom-attributes-using-custom-attributes-04-date.png)
+    ![](../static/target-with-custom-attributes-using-custom-attributes-04-date.png)
 
 ## Using regex with custom attributes
 
@@ -204,7 +204,7 @@ To target users with app version greater than or equal to 4.5 to get the on trea
 (\[5-9\]\\.\[0-9\]|\[4\]\\.\[5-9\]).*
 ```
 
-![](./static/targeting-1.png)
+![](../static/targeting-1.png)
 
 You can use the `matches` operator in the targeting rule editor within the Split user interface to match the value of a passed attribute (such as an app version) to a regular expression and specify treatment assignments for any versions that match the expression.
 
@@ -224,7 +224,7 @@ For this example, to serve the `on` treatment for all employees of split.io, we
 @split.io$
 ```
 
-![](./static/targeting-2.png)
+![](../static/targeting-2.png)
 
 Example of passing the email attribute for the JavaScript SDK:
 
@@ -240,7 +240,7 @@ This example we are serving the on treatment to Chrome users only. However, we w
 Chrome\/[2-9][0-9]\.
 ```
 
-![](./static/targeting-3.png)
+![](../static/targeting-3.png)
 
 Example for the JavaScript SDK:
 
@@ -252,7 +252,7 @@ var attributes = {userAgent: "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/53
 
 An experiment is designed for English speaking users in any country by detecting the default language setting in the browser, the attribute passed is `navigatorLanguage en-`.
 
-![](./static/targeting-4.png)
+![](../static/targeting-4.png)
 
 Example for the JavaScript SDK:
 
@@ -268,7 +268,7 @@ To target an experiment that applies only to users that land on a specific URL,
 http:\/\/mysite\.com\/my_url
 ```
 
-![](./static/targeting-5.png)
+![](../static/targeting-5.png)
 
 Example for the JavaScript SDK:
 
diff --git a/docs/feature-management-experimentation/40-feature-management/target-with-dependencies.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-dependencies.md
similarity index 98%
rename from docs/feature-management-experimentation/40-feature-management/target-with-dependencies.md
rename to docs/feature-management-experimentation/40-feature-management/targeting/target-with-dependencies.md
index 5feede83010..787918eb260 100644
--- a/docs/feature-management-experimentation/40-feature-management/target-with-dependencies.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-dependencies.md
@@ -9,7 +9,7 @@ Use FME's dependency matcher when you want one feature flag to depend on the eva
 
 Let's say we have two feature flags with the second depending on the evaluation of the first. In this example, we would like 20% of customers who receive the on treatment for the *new_test* feature flag to evaluate to on for the *advance_new_feed* feature flag while the other 80% should evaluate to *off*.
 
-![](./static/target-with-dependencies.png)
+![](../static/target-with-dependencies.png)
 
 FME's dependency matcher is located in the condition string to meet your most granular targeting needs.
 
diff --git a/sidebars.ts b/sidebars.ts
index bff891d56e9..4b9307d7e4c 100644
--- a/sidebars.ts
+++ b/sidebars.ts
@@ -1116,13 +1116,85 @@ const sidebars: SidebarsConfig = {
         },
         {
           type: "category",
-          label: "Feature management",
+          label: "Feature Management",
           link: {
-            type: "generated-index",
-            slug: "feature-management-experimentation/feature-management",
+            type: "doc", 
+            id: "feature-management-experimentation/feature-management/index",
           },
-          collapsed: true,
-          items: [ { type: "autogenerated", dirName: "feature-management-experimentation/40-feature-management", }
+          collapsed: false, 
+          items: [
+            {
+              type: "category",
+              label: "Manage Feature Flags",
+              link: {
+                type: "generated-index",
+                title: "Manage Feature Flags",
+                description: "Guides to managing feature flags in Harness FME.",
+              },
+              collapsed: true,
+              items: [
+                "feature-management-experimentation/feature-management/manage-flags/using-essential-scheduling",
+                "feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment",
+                "feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch",
+                "feature-management-experimentation/feature-management/manage-flags/reallocate-traffic",
+                "feature-management-experimentation/feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance",
+                "feature-management-experimentation/feature-management/manage-flags/targeting-an-account",
+                "feature-management-experimentation/feature-management/manage-flags/viewing-user-treatments",
+                "feature-management-experimentation/feature-management/manage-flags/maintaining-consistent-treatments"
+              ],
+            },
+            {
+              type: "category",
+              label: "Monitoring & Analysis",
+              link: {
+                type: "generated-index",
+                title: "Monitoring & Analysis",
+                description: "Monitoring tools and analytics for your feature flags.",
+              },
+              collapsed: true,
+              items: [
+                "feature-management-experimentation/feature-management/monitoring-analysis/customer-dashboard",
+                "feature-management-experimentation/feature-management/monitoring-analysis/impressions",
+                "feature-management-experimentation/feature-management/monitoring-analysis/live-tail",
+                "feature-management-experimentation/feature-management/monitoring-analysis/export-data"
+              ],
+            },
+            {
+              type: "category",
+              label: "Setup",
+              link: {
+                type: "generated-index",
+                title: "Setup",
+                description: "Set up your feature flags and experiments.",
+              },
+              collapsed: true,
+              items: [
+                "feature-management-experimentation/feature-management/setup/create-a-feature-flag",
+                "feature-management-experimentation/feature-management/setup/create-a-rollout-plan",
+                "feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting",
+                "feature-management-experimentation/feature-management/setup/edit-treatments",
+                "feature-management-experimentation/feature-management/setup/dynamic-configurations",
+                "feature-management-experimentation/feature-management/setup/default-treatment",
+                "feature-management-experimentation/feature-management/setup/control-treatment"
+              ],
+            },
+            {
+              type: "category",
+              label: "Targeting",
+              link: {
+                type: "generated-index",
+                title: "Targeting",
+                description: "Target your users precisely with segments and custom attributes.",
+              },
+              collapsed: true,
+              items: [
+                "feature-management-experimentation/feature-management/targeting/segments",
+                "feature-management-experimentation/feature-management/targeting/target-segments",
+                "feature-management-experimentation/feature-management/targeting/limiting-exposure",
+                "feature-management-experimentation/feature-management/targeting/target-with-custom-attributes",
+                "feature-management-experimentation/feature-management/targeting/target-with-dependencies"
+              ],
+            },
           ],
         },
         {

From 977474f2f9899568b8381dce57363c2f6a5f1a68 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Tue, 12 Aug 2025 20:31:21 -0700
Subject: [PATCH 03/11] Fix Links

---
 .../10-getting-started/key-concepts.md               |  6 +++---
 .../overview/build-a-resilient-integration.md        |  4 ++--
 .../overview/create-a-feature-flag.md                |  4 ++--
 .../client-side-sdks/android-sdk.md                  |  8 ++++----
 .../client-side-sdks/angular-utilities.md            |  6 +++---
 .../client-side-sdks/browser-sdk.md                  | 10 +++++-----
 .../client-side-sdks/flutter-plugin.md               |  6 +++---
 .../client-side-sdks/ios-sdk.md                      |  8 ++++----
 .../client-side-sdks/javascript-sdk.md               | 10 +++++-----
 .../client-side-sdks/react-native-sdk.md             |  8 ++++----
 .../client-side-sdks/react-sdk.md                    | 10 +++++-----
 .../client-side-sdks/redux-sdk.md                    |  8 ++++----
 .../client-side-suites/android-suite.md              |  6 +++---
 .../client-side-suites/browser-suite.md              | 10 +++++-----
 .../client-side-suites/ios-suite.md                  |  8 ++++----
 .../server-side-sdks/elixir-thin-client-sdk.md       |  6 +++---
 .../server-side-sdks/go-sdk.md                       | 10 +++++-----
 .../server-side-sdks/java-sdk.md                     | 12 ++++++------
 .../server-side-sdks/net-sdk.md                      | 10 +++++-----
 .../server-side-sdks/nodejs-sdk.md                   | 12 ++++++------
 .../server-side-sdks/php-sdk.md                      |  8 ++++----
 .../server-side-sdks/php-thin-client-sdk.md          |  6 +++---
 .../server-side-sdks/python-sdk.md                   |  8 ++++----
 .../server-side-sdks/ruby-sdk.md                     | 10 +++++-----
 .../using-flag-sets-to-boost-sdk-performance.md      |  2 +-
 .../manage-flags/viewing-user-treatments.md          |  2 +-
 .../monitoring-analysis/customer-dashboard.md        |  2 +-
 .../monitoring-analysis/impressions.md               |  6 +++---
 .../monitoring-analysis/live-tail.md                 |  2 +-
 .../40-feature-management/setup/control-treatment.md |  4 ++--
 .../setup/create-a-feature-flag.md                   |  2 +-
 .../setup/create-a-rollout-plan.md                   |  4 ++--
 .../define-feature-flag-treatments-and-targeting.md  | 12 ++++++------
 .../setup/dynamic-configurations.md                  |  2 +-
 .../targeting/limiting-exposure.md                   |  4 ++--
 .../40-feature-management/targeting/segments.md      |  4 ++--
 .../targeting/target-segments.md                     |  4 ++--
 .../targeting/target-with-custom-attributes.md       |  4 ++--
 .../experimentation-foundations.md                   |  4 ++--
 .../setup/mutually-exclusive-experiments.md          |  2 +-
 .../account-settings/projects.md                     |  4 ++--
 .../account-usage.md                                 |  2 +-
 docs/feature-management-experimentation/api/index.md |  4 ++--
 .../api/webhook/impressions.md                       |  2 +-
 .../shared/metrics/setup/index.mdx                   |  2 +-
 45 files changed, 134 insertions(+), 134 deletions(-)

diff --git a/docs/feature-management-experimentation/10-getting-started/key-concepts.md b/docs/feature-management-experimentation/10-getting-started/key-concepts.md
index 3949e9c122f..3781a5d1c2a 100644
--- a/docs/feature-management-experimentation/10-getting-started/key-concepts.md
+++ b/docs/feature-management-experimentation/10-getting-started/key-concepts.md
@@ -30,7 +30,7 @@ Once your code is deployed, you can instantly turn on or off features for any in
 FME provides visibility into your controlled releases by comparing data about feature flag evaluations with data about what happened after those evaluations. The data points that feed those comparisons are impressions and events. The results of those comparisons are called metrics.
 
 ### Impressions
-An impression is a record of a targeting decision made. It is created automatically each time a feature flag is evaluated and contains details about the user or unique key for which the evaluation was performed, the targeting decision, the targeting rule that drove that decision, and a time stamp. Refer to the [Impressions](/docs/feature-management-experimentation/feature-management/impressions) guide for more information.
+An impression is a record of a targeting decision made. It is created automatically each time a feature flag is evaluated and contains details about the user or unique key for which the evaluation was performed, the targeting decision, the targeting rule that drove that decision, and a time stamp. Refer to the [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) guide for more information.
 
 ### Events
 An event is a record of user or system behavior. Events can be as simple as a page visited, a button clicked, or response time observed, and as complex as a transaction record with a detailed list of properties. An event doesn’t refer to a feature flag. The association between flag evaluations and events is computed for you. An event, associated with a user (or other unique keys), arriving after a flag decision for that same unique key, is attributed to that evaluation by FME’s attribution engine.
@@ -89,13 +89,13 @@ Projects provide separation or partitioning of work to reduce clutter or to enfo
 Within each project, you may have multiple environments, such as development, staging, and production. Refer to the [Environments](/docs/feature-management-experimentation/management-and-administration/fme-settings/environments) guide for more information.
 
 ### Feature flags
-Feature flags are created at the project level where you specify the feature flag name, traffic type, owners, and description. Targeting rules are then created and managed at the environment level as part of the feature flag definition. Refer to the [Feature flag management](/docs/feature-management-experimentation/feature-management/create-a-feature-flag) guide for more information.
+Feature flags are created at the project level where you specify the feature flag name, traffic type, owners, and description. Targeting rules are then created and managed at the environment level as part of the feature flag definition. Refer to the [Feature flag management](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag) guide for more information.
 
 ### Targeting rule
 Targeting rules for each feature flag are created at the environment level. For example, this supports one set of rules in your staging environment and another in production. Rules may be based on user or device attributes, membership in a segment, a percentage of a randomly distributed population, a list of individually specified user or unique key targets, or any combination of the above.
 
 ### Segment
-A segment is a list of users or unique keys for targeting purposes. Segments are created at the environment level. Refer to the [Segments](/docs/feature-management-experimentation/feature-management/segments) guide for more information.
+A segment is a list of users or unique keys for targeting purposes. Segments are created at the environment level. Refer to the [Segments](/docs/feature-management-experimentation/feature-management/targeting/segments) guide for more information.
 
 ### Traffic type
 Targeting decisions are made on a per-user or per unique key basis, but what are the available types of unique keys you intend to target? These are your traffic types, and you can define up to ten unique key types at the project level.
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md b/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
index 31192672b12..d73275d3ed7 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/build-a-resilient-integration.md
@@ -70,7 +70,7 @@ For client-side SDKs, you can set up a listener for the `SDK_READY_TIMED_OUT` ev
 
 ### Account for the control treatment
 
-In case Split is unreachable and the SDK is unable to fetch feature flag definitions‚ and there is no cache available in the case of client side SDKs‚ any evaluations will return the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment). 
+In case Split is unreachable and the SDK is unable to fetch feature flag definitions‚ and there is no cache available in the case of client side SDKs‚ any evaluations will return the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment). 
 
 Make sure you code your application so that it is able to safely handle this situation. This may mean falling back to a safe behavior, such as turning an experimental feature off.
 
@@ -83,7 +83,7 @@ In the status page, you will see the status for Split's various components:
 * **SDK API**: This API serves rollout plans for our SDKs. A service disruption could prevent new SDK instances from initializing if your rollout plans aren't yet cached in Split's CDN as a result of previous requests by other SDK instances, and running SDK instances would not be able to fetch rollout plan changes.
 * **API**: Split's public API.
 * **Web console**: A web console outage means users can't log into Split's web application to modify rollout plans. Existing rollouts plans will continue to be served to Split SDKs in your applications and you end users' experience will be unaffected.
-* **Data processing**: Issues in this component will impact Split's ability to ingest [impression](/docs/feature-management-experimentation/feature-management/impressions) and [event data](/docs/feature-management-experimentation/release-monitoring/events/). This will also affect Live tail, alerts, experimentation, and impression webhooks. SDKs mitigate issues in our data processing pipeline by following a retry mechanism when they fail to post data back to Split. Data processing issues are usually temporal, delaying ingestion and rarely resulting in data loss. 
+* **Data processing**: Issues in this component will impact Split's ability to ingest [impression](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) and [event data](/docs/feature-management-experimentation/release-monitoring/events/). This will also affect Live tail, alerts, experimentation, and impression webhooks. SDKs mitigate issues in our data processing pipeline by following a retry mechanism when they fail to post data back to Split. Data processing issues are usually temporal, delaying ingestion and rarely resulting in data loss. 
 * **Integrations**: This component will reflect issues with any of Split's various [integrations](/docs/feature-management-experimentation/integrations). The integrations affected will be noted in the status page update and if the issue lies with an integration partner it will be noted and tracked.
 * **CDN**: Issues with our CDN may prevent new SDK instances from fetching rollout plans during initialization. For server-side SDKs, this will result in treatment evaluations returning control treatments. For client-side SDKs, evaluations will return treatments according to the rollout plans already cached in the device, if a cache is available.
 * **Streaming Authentication Service**: Issues in this component will prevent SDKs from receiving rollout plan updates via push notifications. In this scenario, all SDKs will fall back to polling to fetch updates with no impact to your end users.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
index ccc7cdead29..52ae607a485 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
@@ -21,7 +21,7 @@ Select the traffic type user to get started. Traffic types give you the ability
 
 Owners, tags, and the description make it easy to sort, filter, and locate the features your team is rolling out. By default, administrators and the creator of the feature flag are considered its owners. Utilize groups with this owner field to organize your flags across your team. Learn more about owners and tags.
 
-For more information about creating a feature flag, see [Create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag/).
+For more information about creating a feature flag, see [Create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag/).
 
 ## Add your feature flag to an environment
 
@@ -60,4 +60,4 @@ if (treatment === 'on') {
 
 To help verify that treatments are being served to your users, click the [Live tail tab](/docs/feature-management-experimentation/feature-management/live-tail/) to view a stream of impressions or SDK evaluations. Impressions occur whenever a visitor is assigned a treatment (i.e., variations) for a feature flag. 
 
-These impressions are generated by the SDKs each time `getTreatment` is called. They are periodically sent back to Split's servers where they are stored and can be accessed for later use. For more information about impressions, see [Impressions](/docs/feature-management-experimentation/feature-management/impressions).
\ No newline at end of file
+These impressions are generated by the SDKs each time `getTreatment` is called. They are periodically sent back to Split's servers where they are stored and can be accessed for later use. For more information about impressions, see [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions).
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/android-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/android-sdk.md
index 1caa03323f0..83318108fa9 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/android-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/android-sdk.md
@@ -48,7 +48,7 @@ implementation 'io.split.client:android-client:5.3.1'
 
 The first time the SDK is instantiated, it starts background tasks to update an in-memory cache and in-storage cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of the data.
 
-If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this circumstance, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this circumstance, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 After the first initialization, the fetched data is stored. Further initializations fetch data from that cache and the configuration is immediately available.
 
@@ -267,7 +267,7 @@ client.on(SplitEvent.SDK_READY_FROM_CACHE, object : SplitEventTask() {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` methods need to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` methods need to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the Rollout plan as defined in Harness FME to decide which treatment is assigned to this key.
 
@@ -557,7 +557,7 @@ val treatmentsByFlagSets = client.getTreatmentsByFlagSets(flagSets)
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `null` for the config parameter.
 
@@ -631,7 +631,7 @@ val treatmentsByFlagSets = client.getTreatmentsWithConfigByFlagSets(flagSetNames
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/angular-utilities.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/angular-utilities.md
index 7108fc9e534..6adc232543d 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/angular-utilities.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/angular-utilities.md
@@ -68,7 +68,7 @@ Configure the service with the SDK key for the FME environment that you would li
 
 ### Basic use
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready, as shown below. You can subscribe to `splitService.sdkReady$` observable provided by splitService before asking for an evaluation.
 
@@ -92,7 +92,7 @@ this.splitService.sdkReady$.subscribe(() => {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the splitService's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the splitService's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -185,7 +185,7 @@ const treatments: SplitIO.Treatments = this.splitService.getTreatmentsByFlagSets
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object with the structure below:
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object with the structure below:
 
 ```javascript title="TypeScript"
 type TreatmentResult = {
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/browser-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/browser-sdk.md
index 48f7e31b657..224c1eee6d1 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/browser-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/browser-sdk.md
@@ -158,7 +158,7 @@ Configure the SDK with the SDK key for the FME environment that you would like t
 
 ### Basic use
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the SDK before asking for an evaluation.
 
@@ -205,7 +205,7 @@ client.on(client.Event.SDK_READY, function() {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -374,7 +374,7 @@ treatments = client.getTreatmentsByFlagSets(flagSets);
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
 
 This method will return an object with the structure below:
 
@@ -479,7 +479,7 @@ treatmentResults = client.getTreatmentsWithConfigByFlagSets(flagSets);
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -764,7 +764,7 @@ const client = factory.client();
 For testing, a developer can put code behind feature flags on their development machine without the SDK requiring network connectivity. To achieve this, the SDK can be started in **localhost** mode (aka off-the-grid or offline mode). In this mode, the SDK neither polls nor updates Harness servers. Instead, it uses an in-memory data structure to determine what treatments to show to the logged in customer for each of the features.
 
 Define the feature flags you want to use in the `features` object map. All `getTreatment` calls for a feature flag now only return the one treatment (and config, if defined) that you have defined in the map.
-Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK was asked to evaluate them.
+Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK was asked to evaluate them.
 
 You can use the additional configuration parameters below when instantiating the SDK in `localhost` mode.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/flutter-plugin.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/flutter-plugin.md
index 945edf6f021..2b6c46e20d9 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/flutter-plugin.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/flutter-plugin.md
@@ -49,7 +49,7 @@ Configure the plugin with the SDK key for the FME environment that you would lik
 
 ### Basic use
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, wait until the SDK is ready, as shown below. You can use the `onReady` parameter when creating the client to get notified when this happens.
 
@@ -72,7 +72,7 @@ _split.client(onReady: (client) async {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), pass the client's `getTreatment` method as an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), pass the client's `getTreatment` method as an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account. The `getTreatment` method supports five types of attributes: strings, numbers, dates, booleans, and sets. The proper data type and syntax for each are:
 
@@ -195,7 +195,7 @@ _split.client(onReady: (client) async {
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` method.
 
 This method returns an object containing the treatment and associated configuration:
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/ios-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/ios-sdk.md
index 605c86a8899..8b66e3cff47 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/ios-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/ios-sdk.md
@@ -50,7 +50,7 @@ Once added, follow the steps provided in the [Carthage Readme](https://github.co
 
 The first time that the SDK is instantiated, it starts background tasks to update an in-memory cache and in-storage cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data.
 
-If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 After the first initialization, the fetched data is stored. Further initializations fetch data from that cache and the configuration is available immediately.
 
@@ -154,7 +154,7 @@ client?.on(event: SplitEvent.sdkReadyFromCache, queue: customQueue) {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the Rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -301,7 +301,7 @@ let treatmentsByFlagSets = client.getTreatmentsByFlagSets(flagSets, attributes:
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` methods. These methods returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` methods. These methods returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `null` for the config parameter.
 
@@ -333,7 +333,7 @@ let treatmentsByFlagSets = client.getTreatmentsWithConfigByFlagSets(flagSets, at
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
index 73868d49bdb..4f9a0c40710 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/javascript-sdk.md
@@ -141,7 +141,7 @@ Configure the SDK with the SDK key for the FME environment that you would like t
  
 ### Basic use
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment). 
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment). 
 
 To make sure the SDK properly loads before asking it for a treatment, block until the SDK is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the SDK before asking for an evaluation. 
 
@@ -186,7 +186,7 @@ client.on(client.Event.SDK_READY, function() {
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -356,7 +356,7 @@ treatments = client.getTreatmentsByFlagSets(flagSets);
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` method.
 
 This method returns an object with the structure below:
 
@@ -481,7 +481,7 @@ treatmentResults = client.getTreatmentsWithConfigByFlagSets(flagSets);
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -720,7 +720,7 @@ For testing, a developer can put code behind feature flags on their development
 
 When instantiating the SDK in localhost mode, your `authorizationKey` is `localhost`. Define the feature flags you want to use in the `features` object map. All `getTreatment` calls for a feature flag now only return the one treatment (and config, if defined) that you have defined in the map.
 
-Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK was asked to evaluate them.
+Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK was asked to evaluate them.
 
 You can use the additional configuration parameters below when instantiating the SDK in `localhost` mode.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-native-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-native-sdk.md
index 5cd10ff8256..e20e9189634 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-native-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-native-sdk.md
@@ -139,7 +139,7 @@ Configure the SDK with the SDK key for the FME environment that you would like t
  
 ### Basic use
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment). 
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment). 
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the SDK before asking for an evaluation. 
 
@@ -200,7 +200,7 @@ LogBox.ignoreLogs(['Setting a timer']);
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -376,7 +376,7 @@ treatments = client.getTreatmentsByFlagSets(flagSets);
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
 
 This method returns an object with the structure below:
 
@@ -691,7 +691,7 @@ For testing, a developer can put code behind feature flags on their development
 
 Define the feature flags you want to use in the `features` object map. All `getTreatment` calls for a feature flag now only return the one treatment (and config, if defined) that you have defined in the map. You can then change the treatment as necessary for your testing. To update a treatment or a config, or to add or remove feature flags from the mock cache, update the properties of the `features` object you've provided. The SDK simulates polling for changes and updates from it. Do not assign a new object to the `features` property because the SDK has a reference to the original object and will not detect the change.
 
-Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK was asked to evaluate them.
+Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK was asked to evaluate them.
 
 You can use the additional configuration parameters below when instantiating the SDK in `localhost` mode.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-sdk.md
index e6943fcbcce..ee6e892933c 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/react-sdk.md
@@ -203,11 +203,11 @@ Configure the SDK with the SDK key for the FME environment that you would like t
 
 ### Get treatments with configurations
 
-When the SDK is instantiated, it kicks off background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it kicks off background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready, as shown below. We provide the `isReady` boolean prop based on the client that will be used by the component. Internally we listen for the `SDK_READY` event triggered by given SDK factory client to set the value of `isReady`.
 
-After the `isReady` prop is set to true, you can use the SDK. The `useSplitTreatments` hook returns the proper treatments based on the `names` prop value passed to it and the `core.key` value you passed in the config when instantiating the SDK. Then use the `treatments` property to access the treatment values as well as the corresponding [dynamic configurations](/docs/feature-management-experimentation/feature-management/dynamic-configurations) that you defined in Harness FME. Remember to handle the client returning control as a safeguard.
+After the `isReady` prop is set to true, you can use the SDK. The `useSplitTreatments` hook returns the proper treatments based on the `names` prop value passed to it and the `core.key` value you passed in the config when instantiating the SDK. Then use the `treatments` property to access the treatment values as well as the corresponding [dynamic configurations](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations) that you defined in Harness FME. Remember to handle the client returning control as a safeguard.
 
 Similarly to the vanilla JS SDK, React SDK supports the ability to evaluate flags based on cached content when using [LOCALSTORAGE](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/javascript-sdk#configuration) as storage type. In this case, the `isReadyFromCache` prop will change to true almost instantly since access to the cache is synchronous, allowing you to consume flags earlier on components that are critical to your UI. Keep in mind that the data might be stale until `isReady` prop is true. Read more [below](#subscribe-to-events-and-changes).
 
@@ -379,7 +379,7 @@ export default class MyComponentToggle extends React.Component {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK needs to be passed an attribute map at runtime. In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the underlying `getTreatmentsWithConfig` or `getTreatmentsWithConfigByFlagSets` call, whether you are evaluating using the `names` or `flagSets` property respectively. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account. The SDK supports five types of attributes: strings, numbers, dates, booleans, and sets. The proper data type and syntax for each are:
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK needs to be passed an attribute map at runtime. In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the underlying `getTreatmentsWithConfig` or `getTreatmentsWithConfigByFlagSets` call, whether you are evaluating using the `names` or `flagSets` property respectively. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account. The SDK supports five types of attributes: strings, numbers, dates, booleans, and sets. The proper data type and syntax for each are:
 
 * **Strings:** Use type String.
 * **Numbers:** Use type Number.
@@ -607,7 +607,7 @@ class MyComponent extends React.Component {
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time an evaluation is done using the `useSplitTreatments` hook. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time an evaluation is done using the `useSplitTreatments` hook. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `useSplitTreatments` hook. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -759,7 +759,7 @@ For testing, a developer can put code behind feature flags on their development
 
 When instantiating the SDK in localhost mode, your `authorizationKey` is `"localhost"`. Define the feature flags you want to use in the `features` object map. All `useSplitTreatments` calls for a feature flag return the treatment (and config, if defined) that you have defined in the map. You can then change the treatment as necessary for your testing. If you want to update a treatment or a config, or to add or remove feature flags from the mock cache, update the properties of the `features` object you've provided. The SDK simulates polling for changes and updates from the `features` object. Do not assign a new object to the `features` property because the SDK has a reference to the original object and will not detect the change.
 
-Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them. Use the following additional configuration parameters when instantiating the SDK in `localhost` mode:
+Any feature that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them. Use the following additional configuration parameters when instantiating the SDK in `localhost` mode:
 
 | **Configuration** | **Description** | **Default value** |
 | --- | --- | --- | 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/redux-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/redux-sdk.md
index fabbc95c7c0..a624cb6fc23 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/redux-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-sdks/redux-sdk.md
@@ -403,7 +403,7 @@ Note that these treatments won't be updated automatically when there is a change
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatments` action creator needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatments` action creator needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatments` action creator call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -525,7 +525,7 @@ const treatments = {
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you don't need to call a specific action creator for your evaluations. Instead, our SDK stores both the treatment and the associated config (or null if there isn't one) in the Redux state. To access this values you can either use the `selectTreatmentWithConfigAndStatus` selector (recommended) or just access the config from the state.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you don't need to call a specific action creator for your evaluations. Instead, our SDK stores both the treatment and the associated config (or null if there isn't one) in the Redux state. To access this values you can either use the `selectTreatmentWithConfigAndStatus` selector (recommended) or just access the config from the state.
 
 Each evaluation entry loaded into the state under the `treatments` key will have the structure below:
 
@@ -603,7 +603,7 @@ const treatmentResult = splitTreatments['key']['feature_flag_1'];
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time an evaluation is done using the `getTreatments` action creator. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time an evaluation is done using the `getTreatments` action creator. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatments` action creator. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -769,7 +769,7 @@ For testing, a developer can put code behind feature flags on their development
 
 When instantiating the SDK in localhost mode, your `authorizationKey` is `"localhost"`. Define the feature flags you want to use in the `features` object map. All feature flag evaluations with `getTreatments` actions return the one treatment (and config, if defined) that you have defined in the map. You can then change the treatment as necessary for your testing. If you want to update a treatment or a config, or to add or remove feature flags from the mock cache, update the properties of the `features` object you've provided. The SDK simulates polling for changes and updates from it. Do not assign a new object to the `features` property because the SDK has a reference to the original object and will not detect the change.
 
-Any feature flag that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them. Use the following additional configuration parameters when instantiating the SDK in `localhost` mode:
+Any feature flag that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them. Use the following additional configuration parameters when instantiating the SDK in `localhost` mode:
 
 | **Configuration** | **Description** | **Default value** |
 | --- | --- | --- | 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/android-suite.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/android-suite.md
index 464cd0587d0..01ccc623d66 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/android-suite.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/android-suite.md
@@ -98,7 +98,7 @@ Configure the Suite with the SDK key for the FME environment that you would like
 
 ### Basic use
 
-When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the Suite is properly loaded before asking it for a treatment, block until the Suite is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the Suite before asking for an evaluation.
 
@@ -163,7 +163,7 @@ client.on(SplitEvent.SDK_READY, object : SplitEventTask() {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -350,7 +350,7 @@ val treatmentsByFlagSets = client.getTreatmentsByFlagSets(flagSets)
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
 
 This method will return an object with the structure below:
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/browser-suite.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/browser-suite.md
index 1cb107050e7..527d5a937cb 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/browser-suite.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/browser-suite.md
@@ -139,7 +139,7 @@ Configure the Suite with the SDK key for the FME environment that you would like
 
 ### Basic use
 
-When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the Suite is properly loaded before asking it for a treatment, block until the Suite is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the Suite before asking for an evaluation.
 
@@ -186,7 +186,7 @@ client.on(client.Event.SDK_READY, function() {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -362,7 +362,7 @@ treatments = client.getTreatmentsByFlagSets(flagSets);
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
 
 This method will return an object with the structure below:
 
@@ -483,7 +483,7 @@ treatmentResults = client.getTreatmentsWithConfigByFlagSets(flagSets);
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the Suite each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the Suite each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the Suite and can provide useful context to the impression data.
 
@@ -791,7 +791,7 @@ For testing, a developer can evaluate FME feature flags on their development mac
 
 Define the feature flags you want to use in the `features` object map. All `getTreatment` calls for a feature flag now only return the one treatment (and config, if defined) that you have defined in the map. You can then change the treatment as necessary for your testing. To update a treatment or a config, or to add or remove feature flags from the mock cache, update the properties of the `features` object you've provided. The SDK simulates polling for changes and updates from it. Do not assign a new object to the `features` property because the SDK has a reference to the original object and will not detect the change.
 
-Any feature flag that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate it.
+Any feature flag that is not provided in the `features` map returns the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate it.
 
 You can use the additional configuration parameters below when instantiating the SDK in `localhost` mode.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/ios-suite.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/ios-suite.md
index 7a0d8d71e58..504c01015bd 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/ios-suite.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/client-side-suites/ios-suite.md
@@ -81,7 +81,7 @@ Configure the Suite with the SDK key for the FME environment that you would like
 
 ### Basic use
 
-When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the Suite is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of the data. If the Suite is asked to evaluate which treatment to show to a user for a specific feature flag while in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the Suite does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the Suite is properly loaded before asking it for a treatment, block until the Suite is ready, as shown below. We set the client to listen for the `SDK_READY` event triggered by the Suite before asking for an evaluation.
 
@@ -106,7 +106,7 @@ client?.on(event: SplitEvent.sdkReady) {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the Suite's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -216,7 +216,7 @@ let treatmentsByFlagSets = client.getTreatmentsByFlagSets(flagSets, attributes:
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the `result.config` property will be `nil`.
 
@@ -247,7 +247,7 @@ let treatmentsByFlagSets = client.getTreatmentsWithConfigByFlagSets(flagSets, at
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/elixir-thin-client-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/elixir-thin-client-sdk.md
index 8bbaa3258d3..eaf54b0d226 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/elixir-thin-client-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/elixir-thin-client-sdk.md
@@ -103,7 +103,7 @@ end
 
 After you start the SDK, you can use the `Split.get_treatment/3` function to decide what version of your features your customers are served. The function requires the `FEATURE_FLAG_NAME` argument that you want to ask for a treatment and a unique `key` argument that corresponds to the end user that you want to serve the feature to.
 
-From there, you simply need to use an if-else-if or case statement block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+From there, you simply need to use an if-else-if or case statement block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 ```elixir title="Elixir"
 ## The key here represents the string ID of the user/account/etc you're trying to evaluate a treatment for
@@ -120,7 +120,7 @@ end
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `get_treatment` function needs to pass an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `get_treatment` function needs to pass an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `get_treatment` call in a map. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -185,7 +185,7 @@ You can also use the [Split Manager](#manager) to get all of your treatments at
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `Split.get_treatment_with_config/3` function. This function returns an `Split.TreatmentWithConfig` struct containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `Split.get_treatment_with_config/3` function. This function returns an `Split.TreatmentWithConfig` struct containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `nil` for the config parameter.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/go-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/go-sdk.md
index 752a3a2996d..1f30915ae1a 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/go-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/go-sdk.md
@@ -69,7 +69,7 @@ Starting version 4.0.0, cfg.BlockUntilReady is deprecated and migrated to the fo
 
 When the SDK is instantiated in `inmemory-standalone` operation mode, it kicks off background tasks to update an in-memory or Redis cache.
 
-This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this circumstance, the SDK does not fail, but instead returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it is in this intermediate state, it may not have the data necessary to run the evaluation. In this circumstance, the SDK does not fail, but instead returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, you need to block until the SDK is ready. You can block by using the `BlockUntilReady(int)` method as part of the instantiation process of the SDK factory client as shown below. Do this as a part of the startup sequence of your application.
 
@@ -106,7 +106,7 @@ Now you can start asking the SDK to evaluate treatments for your customers.
 
 After you instantiate the SDK factory client, you can start using the client's `Treatment` method to decide what version of your feature flags your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you are serving the feature flag to.
 
-Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 ```go title="Go"
 // The key here represents the ID of the user/account/etc you're trying to evaluate a treatment for
@@ -128,7 +128,7 @@ The arguments for the `Treatment()` call are:
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `Treatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `Treatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `Treatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or off` treatment to this account.
 
@@ -194,7 +194,7 @@ treatments := splitClient.TreatmentsByFlagSets("KEY", []string{"backend", "serve
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `TreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `TreatmentWithConfig` method.
 
 This method will return an object containing the treatment and associated configuration.
 
@@ -716,7 +716,7 @@ factory, err := client.NewSplitFactory("localhost", sdkConf)
 
 In this mode, the SDK loads a mapping of feature flag name to treatment from a file at `$HOME/.split`. For a given flag, the treatment specified in the file is returned for every customer. 
 
-`getTreatment` calls for a feature flag and only returns the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `featureFlag` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+`getTreatment` calls for a feature flag and only returns the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `featureFlag` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 The format of this file is two columns separated by a whitespace. The left column is the feature flag name and the right column is the treatment name. The following is a sample `.split` file:
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/java-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/java-sdk.md
index c8e02346ca1..1c984325ddb 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/java-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/java-sdk.md
@@ -72,7 +72,7 @@ Starting version 3.0.1, SplitClientConfig#ready(int) is deprecated and migrated
 * Call `SplitClient#blockUntilReady()` or `SplitManager#blockUntilReady()`.
 :::
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while it's in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready. Do this by setting the desired wait using `.setBlockUntilReadyTimeout()` in the configuration and calling `blockUntilReady()` on the client. Do this all as a part of the startup sequence of your application.
 
@@ -134,7 +134,7 @@ Now you can start asking the SDK to evaluate treatments for your customers.
 
 After you instantiate the SDK factory client, you can start using the `getTreatment` method of the SDK factory client to decide what version of your features your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you are serving the feature to.
 
-Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 <Tabs groupId="java-kotlin-choice">
 <TabItem value="java" label="Java">
@@ -177,7 +177,7 @@ when (treatment) {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the Rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -303,7 +303,7 @@ val treatmentsBySets = client.getTreatmentsByFlagSets("KEY", flagSetNames)
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `null` for the config parameter.
 
@@ -371,7 +371,7 @@ val treatmentsBySets: Map<String, SplitResult> = client.getTreatmentsWithConfigB
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -1031,7 +1031,7 @@ val client: SplitClient = splitFactory.client()
 
 In this mode, the SDK loads a mapping of feature flag name to treatment from a file at `$HOME/.split`. For a given flag, the treatment specified in the file is returned for every customer.
 
-`getTreatment` calls for a feature flag and only returns the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+`getTreatment` calls for a feature flag and only returns the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 The format of this file is two columns separated by a whitespace. The left column is the feature flag name and the right column is the treatment name. The following is a sample `.split` file:
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/net-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/net-sdk.md
index 8567cec495c..063140eb228 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/net-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/net-sdk.md
@@ -42,7 +42,7 @@ Starting version 5.0.0, .Ready is deprecated and migrated to the following imple
 Call `SplitClient.BlockUntilReady(int milliseconds)` or `SplitManager.BlockUntilReady(int milliseconds)`.
 :::
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready. This is done by using `.BlockUntilReady(int milliseconds)` method as part of the instantiation process of the SDK factory client as shown below. Do this all as a part of the startup sequence of your application. If SDK is not ready after the specified time, the SDK fails to initialize and throws a `TimeoutException` error.
 
@@ -76,7 +76,7 @@ Now you can start asking the SDK to evaluate treatments for your customers.
 
 After you instantiate the SDK factory client, you can use the `GetTreatment` method of the SDK factory client to decide what version of your features your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you are serving the feature to.
 
-Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 <Tabs>
 <TabItem value="GetTreatment">
@@ -125,7 +125,7 @@ else
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `GetTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `GetTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `GetTreatment` call. These attributes are compared and evaluated against the attributes used in the Rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -290,7 +290,7 @@ You can also use the [Split Manager](#manager) to get all of your treatments at
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `GetTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `GetTreatmentWithConfig` method.
 
 This method returns an object containing the treatment and associated configuration.
 
@@ -792,7 +792,7 @@ catch (Exception ex)
 
 In this mode, the SDK loads a mapping of feature flag name to treatment from a file at `$HOME/.split`. For a given feature flag, the treatment specified in the file is returned for every customer.
 
-`GetTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+`GetTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 The format of this file is two columns separated by a whitespace. The left column is the feature flag name, and the right column is the treatment name. Here is a sample `.split` file.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/nodejs-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/nodejs-sdk.md
index 0800d1cb727..00ea4eb8114 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/nodejs-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/nodejs-sdk.md
@@ -99,13 +99,13 @@ Use the SDK factory client to evaluate treatments.
 
 ### Basic use
 
-When the SDK is instantiated, it kicks off background jobs to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds. While the SDK is in this intermediate state, if it is asked to evaluate which treatment to show to the logged in customer for a specific feature flag, it may not have data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it kicks off background jobs to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds. While the SDK is in this intermediate state, if it is asked to evaluate which treatment to show to the logged in customer for a specific feature flag, it may not have data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready (as shown below). We set the client to listen for the `SDK_READY` event triggered by the SDK before asking for an evaluation.
 
 When the `SDK_READY` event fires, you can use the `getTreatment` method to return the proper treatment based on the `key` and `FEATURE_FLAG_NAME` attributes you provided.
 
-Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+Then use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning the [control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 <Tabs groupId="java-type-script">
 <TabItem value="JavaScript">
@@ -149,7 +149,7 @@ client.on(client.Event.SDK_READY, () => {
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -283,7 +283,7 @@ If your code runs with both types of storage, read [Working with both sync and a
 
 ### Get treatments with configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method.
 
 This method will return an object containing the treatment and associated configuration.
 
@@ -367,7 +367,7 @@ treatmentResults = client.getTreatmentsWithConfigByFlagSets('user_id', flagSets)
 
 ### Append properties to impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) are generated by the SDK each time a `getTreatment` method is called. These impressions are periodically sent back to Harness servers for feature monitoring and experimentation.
 
 You can append properties to an impression by passing an object of key-value pairs to the `getTreatment` method. These properties are then included in the impression sent by the SDK and can provide useful context to the impression data.
 
@@ -835,7 +835,7 @@ client.on(client.Event.SDK_READY, function() {
 
 In this mode, the SDK loads a mapping of feature flag name to treatment from a file at `$HOME/.split`. For a given feature flag, the treatment specified in the file is returned for every customer. Should you want to use another file, you just need to set the `features` key in the configuration object passed at instantiation time, to the full path of the desired file.
 
-`getTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+`getTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `features` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 Here is a sample `.split` file. The format of this file is two columns separated by a whitespace. The left column is the feature flag name, and the right column is the treatment name.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-sdk.md
index 1ed7b49e3f0..a2d2eb142a5 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-sdk.md
@@ -86,7 +86,7 @@ $splitClient = $splitFactory->client();
 
 After you instantiate the SDK factory client, use the `getTreatment` method of the SDK factory client to decide what version of your feature flags your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you want to serve the feature flag to.
 
-From there, you need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+From there, you need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 ```php title="PHP"
 <?php
@@ -104,7 +104,7 @@ if ($treatment === 'on') {
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to pass an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to pass an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -177,7 +177,7 @@ You can also use the [Split Manager](#manager) to get all of your treatments at
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `null` for the config parameter.
 
@@ -472,7 +472,7 @@ $splitClient = $splitFactory->client();
 
 In this mode, the SDK loads a mapping of feature flag name to treatment from a file at `$HOME/.split`. For a given feature flag, the treatment specified in the file is returned for every customer. 
 
-`getTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `featureFlags` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+`getTreatment` calls for a feature flag only return the one treatment that you defined in the file. You can then change the treatment as necessary for your testing in the file. Any feature flag that is not provided in the `featureFlags` map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 The format of this file is two columns separated by a whitespace. The left column is the feature flag name, and the right column is the treatment name. Here is a sample `.split` file.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-thin-client-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-thin-client-sdk.md
index 92c9a9e0d12..80584ef6955 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-thin-client-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/php-thin-client-sdk.md
@@ -55,7 +55,7 @@ $client = $factory->client();
 
 After you instantiate the SDK factory client, you can start using the `getTreatment` method of the SDK factory client to decide what version of your features your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you want to serve the feature to.
 
-From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 ```php title="PHP"
 <?php
@@ -73,7 +73,7 @@ if ($treatment === 'on') {
 
 ### Attribute syntax 
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `getTreatment` method needs to pass an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `getTreatment` method needs to pass an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `getTreatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -146,7 +146,7 @@ You can also use the [Split Manager](#manager) to get all of your treatments at
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `getTreatmentWithConfig` method. This method returns an object containing the treatment and associated configuration.
 
 The config element is a stringified version of the configuration JSON defined in Harness FME. If there is no configuration defined for a treatment, the SDK returns `null` for the config parameter.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/python-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/python-sdk.md
index ca2b858d804..05f5d58d846 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/python-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/python-sdk.md
@@ -48,7 +48,7 @@ pip install 'splitio_client[cpphash]==10.4.0'
 Starting in version `8.0.0`, readiness has been migrated to a two part implementation. See below for syntax changes you must make if upgrading your SDK to the newest version.
 :::
 
-When the SDK is instantiated in `in-memory` mode, it kicks off background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK doesn't fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated in `in-memory` mode, it kicks off background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK doesn't fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready.
 Since version `8.0.0` This is done by calling the `.block_until_ready()` method in the factory object.
@@ -476,7 +476,7 @@ For further reading about uwsgi decorators and postfork you can take a look at t
 
 After you instantiate the SDK factory client, you can start using the `get_treatment` method of the SDK factory client to decide what version of your feature flags your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `key` attribute that corresponds to the end user that you want to serve the feature to.
 
-From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 <Tabs groupId="python-mode">
 <TabItem value="Multi-threaded">
@@ -517,7 +517,7 @@ If the `key` attribute is something other than `string`, Python SDK returns `CON
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `get_treatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `get_treatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `get_treatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -661,7 +661,7 @@ print(treatments)
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `get_treatment_with_config` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `get_treatment_with_config` method.
 
 This method will return an object containing the treatment and associated configuration.
 
diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/ruby-sdk.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/ruby-sdk.md
index 920e81da98f..76197aaa313 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/ruby-sdk.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/server-side-sdks/ruby-sdk.md
@@ -42,7 +42,7 @@ Since version 2.0.0 of the split-synchronizer, we use a more efficient scheme to
 
 ### 2. Instantiate the SDK and create a new SDK factory client
 
-When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+When the SDK is instantiated, it starts background tasks to update an in-memory cache with small amounts of data fetched from Harness servers. This process can take up to a few hundred milliseconds, depending on the size of data. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. In this case, the SDK does not fail, rather, it returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 To make sure the SDK is properly loaded before asking it for a treatment, block it until the SDK is ready. You can do this by using the `block_until_ready` method of the SDK factory client (or Manager) as part of the instantiation process of the SDK as shown below. Do this as a part of the startup sequence of your application.
 
@@ -145,7 +145,7 @@ If you are running NGINX with `thread_spawn_method = 'smart'`, use our Redis int
 
 After you instantiate the SDK factory client, you can start using the `get_Treatment` method of the SDK factory client to decide what version of your features your customers are served. The method requires the `FEATURE_FLAG_NAME` attribute that you want to ask for a treatment and a unique `KEY` attribute that corresponds to the end user that you want to serve the feature to.
 
-From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment).
+From there, you simply need to use an if-else-if block as shown below and insert the code for the different treatments that you defined in Harness FME. Remember the final else branch in your code to handle the client returning [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment).
 
 ```ruby title="Ruby" 
 ## The key here represents the ID of the user, account, etc. you're trying to evaluate a treatment for
@@ -162,7 +162,7 @@ end
 
 ### Attribute syntax
 
-To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes), the SDK's `get_treatment` method needs to be passed an attribute map at runtime.
+To [target based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes), the SDK's `get_treatment` method needs to be passed an attribute map at runtime.
 
 In the example below, we are rolling out a feature flag to users. The provided attributes `plan_type`, `registered_date`, `permissions`, `paying_customer`, and `deal_size` are passed to the `get_treatment` call. These attributes are compared and evaluated against the attributes used in the rollout plan as defined in Harness FME to decide whether to show the `on` or `off` treatment to this account.
 
@@ -233,7 +233,7 @@ You can also use the [Split Manager](#manager) if you want to get all of your tr
 
 ### Get Treatments with Configurations
 
-To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/dynamic-configurations), you should use the `get_treatment_with_config` method.
+To [leverage dynamic configurations with your treatments](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations), you should use the `get_treatment_with_config` method.
 
 This method will return an object containing the treatment and associated configuration.
 
@@ -499,7 +499,7 @@ In the example given, a call to `get_treatment` passing `john_doe` as the key re
     config: {'desc': 'this applies only to ON and only for john_doe and jane_doe. The rest will receive OFF'}
 ```
 
-Any feature that is not provided in the `split_file` markup map returns [the control treatment](/docs/feature-management-experimentation/feature-management/control-treatment) if the SDK is asked to evaluate them.
+Any feature that is not provided in the `split_file` markup map returns [the control treatment](/docs/feature-management-experimentation/feature-management/setup/control-treatment) if the SDK is asked to evaluate them.
 
 By default, changes in the file are not automatically picked up without restarting the client. To have the client automatically pick up changes to the file, specify `reload_rate` as the interval in seconds at which changes are picked up. Here is an example of specifying both `split_file` and `reload_rate`.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
index eb71cbf4de8..d1e175463d3 100644
--- a/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance.md
@@ -51,5 +51,5 @@ To use flag sets you need to:
    | per feature flag definition * | Service | 3 | 3 | 5 | 5 | 5 |
    | filtered by a single SDK instance | Service | 10 | 10 | 10 | 10 | 10 |
 
-* An FME feature flag can have a definition for each Environment in your Project. For more information see [Create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag).
+* An FME feature flag can have a definition for each Environment in your Project. For more information see [Create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag).
 
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
index c5cf5cc3997..d68ac7b8f48 100644
--- a/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/viewing-user-treatments.md
@@ -22,7 +22,7 @@ In this image, you can see that the user will see the **'on'** treatment for the
 
 ### Caveats
 
-- This view only shows treatments for feature flags **not using [attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/)** for targeting.  
+- This view only shows treatments for feature flags **not using [attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/)** for targeting.  
 - For data privacy reasons, Split does **not copy attribute values** to the cloud, so it cannot know attribute values for a given user key (user ID).  
 - The key shown is typically a database ID or a cookie ID.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
index 451f3c325cb..f0401f87e37 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/customer-dashboard.md
@@ -17,7 +17,7 @@ The first tab shows the treatments that are served to that customer for each fea
 
 ## Segments 
 
-The second tab shows the segments that a particular user belongs to or if that user is not part of any segments. Learn more about [creating segments](/docs/feature-management-experimentation/feature-management/segments). 
+The second tab shows the segments that a particular user belongs to or if that user is not part of any segments. Learn more about [creating segments](/docs/feature-management-experimentation/feature-management/targeting/segments). 
 
 ## Attributes
 
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
index 5e010b0aafe..e3a1d495b36 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
@@ -5,7 +5,7 @@ description: ""
 sidebar_position: 18
 redirect_from:
 - /docs/feature-management-experimentation/feature-management/faqs/is-the-feature-flag-impression-toggle-supported
-- /docs/feature-management-experimentation/feature-management/impressions
+- /docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions
 ---
 
 import Tabs from '@theme/Tabs';
@@ -153,8 +153,8 @@ Usually, a treatment is served because the customer matched a particular rule. I
 | --- | --- | --- |
 | Feature flag was killed | [Default](/docs/feature-management-experimentation/feature-management/default-treatment) | `killed` |
 | No rule matched | [Default](/docs/feature-management-experimentation/feature-management/default-treatment) | `no rule matched` |
-| Feature flag was not found, for example, it may not have been downloaded by the SDK yet | [Control](/docs/feature-management-experimentation/feature-management/control-treatment) | `definition not found` |
-| There was an exception while evaluating treatment | [Control](/docs/feature-management-experimentation/feature-management/control-treatment) | `exception`  |
+| Feature flag was not found, for example, it may not have been downloaded by the SDK yet | [Control](/docs/feature-management-experimentation/feature-management/setup/control-treatment) | `definition not found` |
+| There was an exception while evaluating treatment | [Control](/docs/feature-management-experimentation/feature-management/setup/control-treatment) | `exception`  |
 
 If your impression tables are showing *not available*, consider upgrading your SDK and ensure that labels are enabled in the SDK configurations.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
index 480c9ac98ff..c9e1e7b3bb3 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
@@ -37,7 +37,7 @@ After installing Split’s SDKs and setting up a feature flag, impressions gener
 
 Live tail can also be accessed on the feature flag page under the Live tail tab, where impressions automatically filter down to that specific feature flag.
 
-Learn more about [impressions](/docs/feature-management-experimentation/feature-management/impressions) in FME.
+Learn more about [impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) in FME.
 
 ## Query events 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md b/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
index 2557092bb22..b2f74b904c2 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/control-treatment.md
@@ -4,7 +4,7 @@ sidebar_label: Control treatment
 description: ""
 sidebar_position: 13
 redirect_from:
-  - /docs/feature-management-experimentation/feature-management/control-treatment
+  - /docs/feature-management-experimentation/feature-management/setup/control-treatment
 ---
 
 The **control** treatment is a reserved treatment in FME. You cannot create a treatment named **control**. The **control** treatment is returned by the SDK in the following scenarios:
@@ -22,7 +22,7 @@ The **control** treatment is a reserved treatment in FME. You cannot create a tr
 
 * **The SDK evaluated an unsupported targeting rule.** FME SDKs are distributed as versioned packages to be consumed by applications. As new targeting rules are added to feature flags in Harness FME, the FME SDKs are also updated to support the new targeting rules. If an SDK encounters a new targeting rule that it cannot evaluate, it returns the **control** treatment for that feature flag evaluation. In this case, a special FME feature flag evaluation impression is generated with a targeting rule label of "targeting rule type unsupported by sdk". This impression is sent to Harness cloud and can be seen in Live tail in Data hub.
 
-SemVer (Semantic Version) targeting rules are supported by FME SDKs and customer-deployed components. See [Target with custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes#supported-sdks-and-customer-deployed-components-for-semver-matcher) to verify the minimum FME SDK and customer-deployed component versions for SemVer support.
+SemVer (Semantic Version) targeting rules are supported by FME SDKs and customer-deployed components. See [Target with custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes#supported-sdks-and-customer-deployed-components-for-semver-matcher) to verify the minimum FME SDK and customer-deployed component versions for SemVer support.
 
 :::tip[Tip]
 When evaluating which treatment to show to a customer, Harness recommends handling the *control* treatment in the same way as the default treatment.
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
index 9da09ee8777..c386f1f3338 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
@@ -5,7 +5,7 @@ description: Learn how to create a feature flag in Harness FME.
 sidebar_position: 1
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/faqs/cannot-delete-feature-flag-from-ui-even-after-deleting-the-targeting-rules
-  - /docs/feature-management-experimentation/feature-management/create-a-feature-flag
+  - /docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
index 0570f7e17d2..4f806bf803c 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
@@ -194,7 +194,7 @@ When deciding whether to use segments or attributes, segments are best when cust
 The instructions in this section explain the proper use of attributes when you create rollout plans in the Split user interface. To understand the proper syntax for using attributes with the SDK in your code base, refer to the relevant language-specific article in our SDK documentation.
 
 1. In the **Targeting** section, click the **attribute** field. A list of attributes displays.
-1. Select or add an attribute. Split requires you to customize the attributes you use in your treatment evaluation. When you define your targeting rules, you can base your rules on any [custom attribute](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/) you provide. To do this, add an attribute in the field provided. 
+1. Select or add an attribute. Split requires you to customize the attributes you use in your treatment evaluation. When you define your targeting rules, you can base your rules on any [custom attribute](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/) you provide. To do this, add an attribute in the field provided. 
 1. Select the matcher. When you have input a custom attribute, you are prompted to select a matcher. For more information about matchers, refer to the Types and matchers for custom attributes section below.
 1. Select the attribute values for the attribute that you want Split to match after you select a matcher. Below are some examples of usage for different types of attributes:
    
@@ -238,7 +238,7 @@ Parent feature flags know which child feature flags are using it in their evalua
 
 #### Serving treatments to a percentage using a segment
 
-Using [segments](/docs/feature-management-experimentation/feature-management/segments) in targeting rules, rather than individually targeting, serves a treatment to a percentage of that segment. Use segments for targeting relatively fixed or specific groups of users that you can identify, like a list of accounts. For example, you can gradually roll out a treatment to a segment of your beta users. 
+Using [segments](/docs/feature-management-experimentation/feature-management/targeting/segments) in targeting rules, rather than individually targeting, serves a treatment to a percentage of that segment. Use segments for targeting relatively fixed or specific groups of users that you can identify, like a list of accounts. For example, you can gradually roll out a treatment to a segment of your beta users. 
 
 ### Selecting treatment to serve 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
index 195183977d2..dc8868b37a5 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
@@ -11,7 +11,7 @@ redirect_from:
 
 ## Define feature flag treatments and targeting
 
-After you [create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag), you can define treatments, dynamic configurations, and targeting rules. 
+After you [create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag), you can define treatments, dynamic configurations, and targeting rules. 
 
 This allows you to deploy features for internal testing and beta releases. You can also progressively roll out features to your production environment.
 
@@ -21,7 +21,7 @@ This article provides an overview of creating a feature flag definition for a gi
 
 ## Create a feature flag
 
-On the [Create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag) pane, create a feature flag by filling in metadata useful to your team.
+On the [Create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag) pane, create a feature flag by filling in metadata useful to your team.
 
 ## Initiate your feature flag definition for a given environment
 
@@ -56,7 +56,7 @@ In the [Treatments](/docs/feature-management-experimentation/feature-management/
 
 ## Setting up dynamic configuration
 
-In the [Dynamic configuration](/docs/feature-management-experimentation/feature-management/dynamic-configurations) area, optionally define custom JSON or Key-value pairs that you can retrieve in your code. This allows you to add dynamic configurations to your feature variations.
+In the [Dynamic configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations) area, optionally define custom JSON or Key-value pairs that you can retrieve in your code. This allows you to add dynamic configurations to your feature variations.
 
 ## Setting up targeting
 
@@ -74,7 +74,7 @@ Here are a few examples:
 Be mindful of using random user IDs, as [overuse of random IDs](/docs/feature-management-experimentation/management-and-administration/admin-best-practices/mtk-efficiency#use-of-unstable-ids) can increase your MTK count (and [costs](/docs/feature-management-experimentation/management-and-administration/account-usage)) unnecessarily.
 :::
 
-Targeting rules have a limit of 500 user IDs, so we suggest [creating a segment](/docs/feature-management-experimentation/feature-management/segments) if you need to target a large number of user IDs.
+Targeting rules have a limit of 500 user IDs, so we suggest [creating a segment](/docs/feature-management-experimentation/feature-management/targeting/segments) if you need to target a large number of user IDs.
 
 To set up targeting, you can use the Individual targets area and/or the Targeting rules area. Both are described below.
 
@@ -96,7 +96,7 @@ In the Targeting rules area, you can:
 
   Within attribute based targeting rules, you can:
 
-    * [Target with custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes) to target specific subsets of your users based on a specific attribute (e.g., location or last-login date).
+    * [Target with custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes) to target specific subsets of your users based on a specific attribute (e.g., location or last-login date).
 
     * [Target with dependencies](/docs/feature-management-experimentation/feature-management/target-with-dependencies) to target users based on the treatment they received from another flag. You can use flag dependencies to create mutually exclusive experiments. 
 
@@ -194,7 +194,7 @@ This is especially important when you have more than three treatments. For insta
 | treatment3        | 60-79   | treatment3                     |
 | treatment4        | 80-99   | treatment4                     |
 
-One way to avoid bucket shifts while moving users between treatments is to use [Dynamic Configuration](/docs/feature-management-experimentation/feature-management/dynamic-configurations/). You keep bucket assignments the same, but change the configuration values of treatments.
+One way to avoid bucket shifts while moving users between treatments is to use [Dynamic Configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations/). You keep bucket assignments the same, but change the configuration values of treatments.
 
 | Even Distribution | Buckets | Dynamic Configuration |
 | ----------------- | ------- | --------------------- |
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md b/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
index b68f0a3ce90..fb8503fe337 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/dynamic-configurations.md
@@ -5,7 +5,7 @@ description: "Learn how to use Dynamic Configuration in feature flags, enabling
 sidebar_position: 5
 redirect_from:
   - /docs/feature-management-experimentation/feature-management/faqs/dynamic-configuration-section-does-not-show-up/
-  - /docs/feature-management-experimentation/feature-management/dynamic-configurations
+  - /docs/feature-management-experimentation/feature-management/setup/dynamic-configurations
 ---
 
 ## Overview
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md b/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
index 4a665229360..8ea74c76f94 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/limiting-exposure.md
@@ -5,7 +5,7 @@ description: ""
 sidebar_position: 8
 ---
 
-When you [create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag), you can limit the feature flag's exposure. Limiting exposure means limiting the percentage of traffic that is channeled through a feature flag, allowing the rest of the traffic _to be unaffected by_, or _to remain outside of_, the targeting rules. The users outside of the flag's targeting rules will be given the default treatment.
+When you [create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag), you can limit the feature flag's exposure. Limiting exposure means limiting the percentage of traffic that is channeled through a feature flag, allowing the rest of the traffic _to be unaffected by_, or _to remain outside of_, the targeting rules. The users outside of the flag's targeting rules will be given the default treatment.
 
 To use this feature, do the following:
 
@@ -22,4 +22,4 @@ The Limit exposure feature is particularly useful for experimentation, where you
 
 Note that the treatment assigned a user ID according to the feature flag's targeting rules will be "sticky", meaning that increasing the Limit exposure percentage will not reallocate an already assigned user to a different treatment.
 
-A user that is outside the exposed traffic is assigned the default treatment and the feature flag's [impression](/docs/feature-management-experimentation/feature-management/impressions) for that evaluation will include the _**targeting label**_ `not in split`.
\ No newline at end of file
+A user that is outside the exposed traffic is assigned the default treatment and the feature flag's [impression](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) for that evaluation will include the _**targeting label**_ `not in split`.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/segments.md
index b7f49dc0b1d..78fb441173f 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/segments.md
@@ -18,7 +18,7 @@ A segment is a pre-defined list of user IDs (user keys) that a feature flag can
 * be created and edited via the Split API
 * [sync with any Amplitude Cohort](https://www.split.io/product/integrations/amplitude/)
 
-Segments are best for targeting relatively fixed or specific groups of users that you can easily identify, like individual accounts. If you would like to learn about more dynamic targeting, refer to the [Custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes) guide.
+Segments are best for targeting relatively fixed or specific groups of users that you can easily identify, like individual accounts. If you would like to learn about more dynamic targeting, refer to the [Custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes) guide.
 
 For feature releases, you can use segments to:
 
@@ -33,7 +33,7 @@ These are a few examples of using segments to deploy safe and successful feature
 Harness FME supports the following segment types:
   * **Standard segments** - can contain up to 100,000 user IDs
   * **Large segments** - can contain up to 1,000,000 user IDs (may be increased upon request)
-  * **Rule-based segments** - dynamically include user IDs based on [targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting/#targeting-rules) 
+  * **Rule-based segments** - dynamically include user IDs based on [targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#targeting-rules) 
 
 :::info
 **Large segments** are available on the [Enterprise](https://www.split.io/pricing/) plan.
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index f73e0e7430a..1b628265781 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -7,11 +7,11 @@ sidebar_position: 7
 
 Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md#setting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
 
-[Segments](/docs/feature-management-experimentation/feature-management/segments) are lists of user IDs. On your feature flag Definition tab, you can create targeting rules that assign treatments to segments.
+[Segments](/docs/feature-management-experimentation/feature-management/targeting/segments) are lists of user IDs. On your feature flag Definition tab, you can create targeting rules that assign treatments to segments.
 
 This article shows how to target Standard, Large, and Rule-based segments in your individual targeting rules and in attribute based targeting rules.
 
-___Tip:___ If you don't see a segment appear when you type its name in the segment input field, it may be that the segment is not [defined](/docs/feature-management-experimentation/feature-management/segments#adding-user-ids-to-a-segment) for the given FME environment.
+___Tip:___ If you don't see a segment appear when you type its name in the segment input field, it may be that the segment is not [defined](/docs/feature-management-experimentation/feature-management/targeting/segments#adding-user-ids-to-a-segment) for the given FME environment.
 
 :::warning[Server-side SDK support for Large segments]
 Server-side SDKs do not yet support Large segments, but soon will. Until they are supported, evaluations of feature flags that target Large segments will return `control` on server-side SDKs. 
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
index f476579b45f..850ead1029e 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
@@ -17,7 +17,7 @@ With custom attributes, you can create dynamic targeted feature rollout plans us
 * Sensitive information (e.g., customer purchase size or customer status)
 
 :::tip
-Consider using [segments](/docs/feature-management-experimentation/feature-management/segments) (instead of attributes) if the users in a segment would not change multiple times in a day or the grouping of users needs to be standardized across your Harness account (e.g., key accounts, internal or outsourced QA teams, or company employees).
+Consider using [segments](/docs/feature-management-experimentation/feature-management/targeting/segments) (instead of attributes) if the users in a segment would not change multiple times in a day or the grouping of users needs to be standardized across your Harness account (e.g., key accounts, internal or outsourced QA teams, or company employees).
 :::
 
 ## Creating custom attributes
@@ -142,7 +142,7 @@ As with attributes [created in Admin settings](#creating-custom-attributes-in-ad
 
 ## Using custom attributes in feature flag targeting
 
-After you [create a feature flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag) you can **use** (and also **create**) custom attributes in your [targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting). To add an attribute-based targeting rule to a feature flag:
+After you [create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag) you can **use** (and also **create**) custom attributes in your [targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting). To add an attribute-based targeting rule to a feature flag:
 
 1. In Harness FME, on the Definition tab of a feature flag, in the Targeting rules area, click the **Add attribute based targeting rules** button. The **IF** field/dropdown menu appears.
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md b/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
index 13f04296cc4..aa3c680002f 100644
--- a/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
+++ b/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
@@ -65,10 +65,10 @@ In order to get your experiments live as quickly and accurately as possible, it
 
 See the following documentation:
 
-- [Create a Feature Flag](/docs/feature-management-experimentation/feature-management/create-a-feature-flag#create-a-feature-flag)
+- [Create a Feature Flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag#create-a-feature-flag)
 - [Create a Metric](/docs/feature-management-experimentation/release-monitoring/metrics/setup/#create-a-metric)
 - [Edit Treatments](/docs/feature-management-experimentation/feature-management/edit-treatments/)
-- [Add Targeting Rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting/#targeting-rules)
+- [Add Targeting Rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#targeting-rules)
 - [Adjust Experiment Settings](/docs/feature-management-experimentation/experimentation/setup/experiment-settings)
 
 ## Experiment Ideation 
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
index ec2eda63100..a3cc153e428 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
@@ -107,7 +107,7 @@ Mutually exclusive experiments carry some inherent limitations. You should be aw
 
 ### Ending an experiment and reallocating users
 
-The Harness FME feature flag (parent flag) that divides traffic between mutually exclusive experiments uses a random and deterministic hashing algorithm to allocate traffic. Suppose you want to end one of the concurrent mutually exclusive experiments and reallocate the users to the other experiments (and you are sure this will not invalidate your results). To learn how your traffic would be reallocated, see [How does Harness FME ensure a consistent user experience?](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience). You may also consider using [dynamic configuration](/docs/feature-management-experimentation/feature-management/dynamic-configurations/) in your flag definitions if you anticipate ending a mutually exclusive experiment early.
+The Harness FME feature flag (parent flag) that divides traffic between mutually exclusive experiments uses a random and deterministic hashing algorithm to allocate traffic. Suppose you want to end one of the concurrent mutually exclusive experiments and reallocate the users to the other experiments (and you are sure this will not invalidate your results). To learn how your traffic would be reallocated, see [How does Harness FME ensure a consistent user experience?](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience). You may also consider using [dynamic configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations/) in your flag definitions if you anticipate ending a mutually exclusive experiment early.
 
 :::note 
 We generally do not recommend re-using the divisions created by the parent feature flag for new experiments, because you would be experimenting with the subset of your user population that was exposed to an earlier experiment. This subset may not be representative of your entire user population.
diff --git a/docs/feature-management-experimentation/70-management-and-administration/account-settings/projects.md b/docs/feature-management-experimentation/70-management-and-administration/account-settings/projects.md
index 643df02f88c..a16910a3ec4 100644
--- a/docs/feature-management-experimentation/70-management-and-administration/account-settings/projects.md
+++ b/docs/feature-management-experimentation/70-management-and-administration/account-settings/projects.md
@@ -9,8 +9,8 @@ Projects allow you to separately manage your feature flags and experiments acros
 
 The following objects are also located in a project. Refer object model map below for reference:
 
-* [Feature flags](/docs/feature-management-experimentation/feature-management/create-a-feature-flag/)
-* [Segments](/docs/feature-management-experimentation/feature-management/segments)
+* [Feature flags](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag/)
+* [Segments](/docs/feature-management-experimentation/feature-management/targeting/segments)
 * [Metrics](/docs/feature-management-experimentation/release-monitoring/metrics/)
 * [Event types](/docs/feature-management-experimentation/release-monitoring/events/)
 * [API keys](/docs/feature-management-experimentation/management-and-administration/account-settings/api-keys)  
diff --git a/docs/feature-management-experimentation/70-management-and-administration/account-usage.md b/docs/feature-management-experimentation/70-management-and-administration/account-usage.md
index bf22f0146fc..5dff93450d9 100644
--- a/docs/feature-management-experimentation/70-management-and-administration/account-usage.md
+++ b/docs/feature-management-experimentation/70-management-and-administration/account-usage.md
@@ -22,7 +22,7 @@ An MTK count is the number of unique keys that were evaluated for treatments ove
 
 ### Impressions
 
-[Impressions](/docs/feature-management-experimentation/feature-management/impressions/) are a unique event record generated every time a monthly tracked key calls the FME SDK to get a treatment evaluation.
+[Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions/) are a unique event record generated every time a monthly tracked key calls the FME SDK to get a treatment evaluation.
 
 ### Events
 
diff --git a/docs/feature-management-experimentation/api/index.md b/docs/feature-management-experimentation/api/index.md
index 97f998b5480..d7898463fb7 100644
--- a/docs/feature-management-experimentation/api/index.md
+++ b/docs/feature-management-experimentation/api/index.md
@@ -19,10 +19,10 @@ You can use the Split Admin API to:
 :::warning Not used for `targeting` and `getTreatment`
 Customer attributes stored using the methods discussed here aren't used for targeting your roll out definitions or as part of the standard usage of `getTreatment`. In order to target, you must pass the attribute with the `getTreatment` call. 
 
-Learn more about [targeting based on custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/).
+Learn more about [targeting based on custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/).
 :::
 
-You can use the [Identities REST API](https://docs.split.io/reference/identities-overview) to identify your keys in Split, making them available when writing targeting rules in the user interface and enriching autocomplete functionality throughout the user interface. Identifying your keys is useful if you want to pre-populate the user interface for your team and provide customer data when setting up your rollouts and analyzing [impression data](/docs/feature-management-experimentation/feature-management/impressions/).
+You can use the [Identities REST API](https://docs.split.io/reference/identities-overview) to identify your keys in Split, making them available when writing targeting rules in the user interface and enriching autocomplete functionality throughout the user interface. Identifying your keys is useful if you want to pre-populate the user interface for your team and provide customer data when setting up your rollouts and analyzing [impression data](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions/).
 
 Split offers both an Identities API and [client-specific API wrappers](/docs/feature-management-experimentation/api/wrappers/python-admin-api) to use when sending to Split attributes on your keys. As an administrator in Split, you can see the attributes that your team is sending to Split and the last time an update is received.
 
diff --git a/docs/feature-management-experimentation/api/webhook/impressions.md b/docs/feature-management-experimentation/api/webhook/impressions.md
index 6dfc5eacfce..791df28b99b 100644
--- a/docs/feature-management-experimentation/api/webhook/impressions.md
+++ b/docs/feature-management-experimentation/api/webhook/impressions.md
@@ -9,7 +9,7 @@ sidebar_label: Impressions
 
 Use this outgoing webhook to integrate Split impressions into the tools that your team already uses. You can use Split webhooks to enhance your tracking, and analytics tools to provide insights around the impact of feature changes.
 
-Whenever Split stores down an [impression](/docs/feature-management-experimentation/feature-management/impressions/) from an SDK, we send an HTTP POST payload to the webhook's configured URL in JSON format (`Content-Type: application/json`) in batches of impressions seen approximately every 10 seconds. The data sent has the following schema. Any response code other than 200 is marked as failure.
+Whenever Split stores down an [impression](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions/) from an SDK, we send an HTTP POST payload to the webhook's configured URL in JSON format (`Content-Type: application/json`) in batches of impressions seen approximately every 10 seconds. The data sent has the following schema. Any response code other than 200 is marked as failure.
 
 To reduce latency, each HTTP POST is be gzipped unless the URL represents a Slack or [Fivetran](https://fivetran.com/) webhook.
 
diff --git a/docs/feature-management-experimentation/shared/metrics/setup/index.mdx b/docs/feature-management-experimentation/shared/metrics/setup/index.mdx
index af9f14dd331..39bf1241c00 100644
--- a/docs/feature-management-experimentation/shared/metrics/setup/index.mdx
+++ b/docs/feature-management-experimentation/shared/metrics/setup/index.mdx
@@ -11,7 +11,7 @@ Once you begin collecting and analyzing your data, you’re likely to find it ea
 
 Metrics can be created in Split when you have two types of data: impressions and events.
 
-* [Impressions](/docs/feature-management-experimentation/feature-management/impressions) occur when a visitor is assigned a treatment (i.e., variations) for a feature flag.
+* [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions) occur when a visitor is assigned a treatment (i.e., variations) for a feature flag.
 * [Events](/docs/feature-management-experimentation/experimentation/events/) track information and actions a user takes while in that feature flag.
 
 You can [create a metric](#create-a-metric), monitor, and measure impact in six steps: 

From 249fff1bd68746858b4cff2d6ef09ffc762cf6b8 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Tue, 12 Aug 2025 21:06:20 -0700
Subject: [PATCH 04/11] Fix More Links

---
 .../overview/create-a-feature-flag.md                  |  6 +++---
 .../40-feature-management/index.md                     |  2 +-
 .../manage-flags/reallocate-traffic.md                 |  2 +-
 .../monitoring-analysis/impressions.md                 |  2 +-
 .../monitoring-analysis/live-tail.md                   |  2 +-
 .../setup/create-a-feature-flag.md                     |  2 +-
 .../setup/create-a-rollout-plan.md                     |  6 +++---
 .../define-feature-flag-treatments-and-targeting.md    | 10 +++++-----
 .../40-feature-management/targeting/target-segments.md |  2 +-
 .../targeting/target-with-custom-attributes.md         |  2 +-
 .../dimensional-analysis.md                            |  2 +-
 .../analyzing-experiment-results/health-check.mdx      |  2 +-
 .../experiment-results/reallocate-traffic.md           |  2 +-
 .../60-experimentation/experimentation-foundations.md  |  2 +-
 .../60-experimentation/setup/a-a-testing.md            |  4 ++--
 .../setup/mutually-exclusive-experiments.md            |  2 +-
 .../shared/alert-policies/index.mdx                    |  2 +-
 .../shared/events/index.mdx                            |  4 ++--
 18 files changed, 28 insertions(+), 28 deletions(-)

diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
index 52ae607a485..2007b5745dd 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
@@ -31,7 +31,7 @@ To configure your feature flag for a particular environment, select the environm
 
 Treatments are the different variants or versions of your feature flag that you serve to your users. When you click the **Initiate environment** button, the Definition tab for a particular flag appears. Use this tab to assign your treatments. 
 
-We default the treatment names to on and off for each new feature flag but you can edit these names and add additional treatments. The default treatment selected in the treatments section will be served to everyone if the feature flag is killed and to all traffic not exposed to a flag. For more information about the default treatment, see [Set the default treatment](/docs/feature-management-experimentation/feature-management/set-the-default-treatment).
+We default the treatment names to on and off for each new feature flag but you can edit these names and add additional treatments. The default treatment selected in the treatments section will be served to everyone if the feature flag is killed and to all traffic not exposed to a flag. For more information about the default treatment, see [Set the default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment).
 
 After your treatments are set up and the default treatment is chosen, you can set individual targets, limit exposure, and set targeting rules to explicitly assign treatments or set targets based on dependencies or demographic data as attributes.
 
@@ -39,7 +39,7 @@ After your treatments are set up and the default treatment is chosen, you can se
 * Limit exposure (advanced): Allows you to randomly assign a percentage of your users to be evaluated by all the targeting rules that are not individual targets. This feature is recommended for advanced experimentation use cases.
 * Set targeting rules: Allows you to build out if/else statements to use demographic data as attributes to assign treatments to users and  build dependencies with other features you manage in Split.
 
-Click **Save changes** to configure the rules for this feature flag in the environment you selected. Learn more about [targeting](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting).
+Click **Save changes** to configure the rules for this feature flag in the environment you selected. Learn more about [targeting](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting).
 
 Now that you've set up this feature flag's targeting rules, let's take a look at how this flag would work in your code. To implement this feature flag, copy and wrap the provided code snippet around your feature's treatments. 
 
@@ -58,6 +58,6 @@ if (treatment === 'on') {
 }
 ```
 
-To help verify that treatments are being served to your users, click the [Live tail tab](/docs/feature-management-experimentation/feature-management/live-tail/) to view a stream of impressions or SDK evaluations. Impressions occur whenever a visitor is assigned a treatment (i.e., variations) for a feature flag. 
+To help verify that treatments are being served to your users, click the [Live tail tab](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail/) to view a stream of impressions or SDK evaluations. Impressions occur whenever a visitor is assigned a treatment (i.e., variations) for a feature flag. 
 
 These impressions are generated by the SDKs each time `getTreatment` is called. They are periodically sent back to Split's servers where they are stored and can be accessed for later use. For more information about impressions, see [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions).
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/index.md b/docs/feature-management-experimentation/40-feature-management/index.md
index e10667b259d..a37232a6a02 100644
--- a/docs/feature-management-experimentation/40-feature-management/index.md
+++ b/docs/feature-management-experimentation/40-feature-management/index.md
@@ -69,7 +69,7 @@ Limit exposure controls participation in targeting rules:
 
 ### Treatment Order Matters
 
-The order in which treatments are defined can affect the consistency of user experience. [Learn more about treatment order](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#treatment-ordering-and-traffic-distribution).
+The order in which treatments are defined can affect the consistency of user experience. [Learn more about treatment order](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#treatment-ordering-and-traffic-distribution).
 
 ### Traffic Type and Targeting Granularity
 
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
index dc4b231a799..cc9d58fe0fa 100644
--- a/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/reallocate-traffic.md
@@ -12,7 +12,7 @@ When targeting rules are defined for a feature flag, the rules are used to assig
 In some circumstances, it is useful to reassign customers to treatments without changing the targeting rules. This process is called *reallocate* in FME. Here is an example.
 
 * Your team enables a particular treatment for 10% of customers.
-* During the release, a critical issue is found and the feature flag is immediately [killed](/docs/feature-management-experimentation/feature-management/use-the-kill-switch), sending all customers to the default treatment. 
+* During the release, a critical issue is found and the feature flag is immediately [killed](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch), sending all customers to the default treatment. 
 * Your team fixes the bug and wants to release again to 10% of customers.
 * Your team does not want the same 10% to see the same treatment again, because they were exposed to the original issue. In other words, those customers were *poisoned* with regards to this treatment. 
 
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
index e3a1d495b36..8a1229daa45 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
@@ -125,7 +125,7 @@ Use the following tables to verify that your SDK or customer-deployed component
 
 ## Viewing impressions in Harness
 
-You can view incoming impressions in [Live tail](/docs/feature-management-experimentation/feature-management/live-tail) within a feature flag's page or the Data hub, and export them based on query criteria in the Data hub's [Data export tab](/docs/feature-management-experimentation/feature-management/export-data). If you do not see impressions arriving in Harness, ensure that your SDK is installed and functioning as expected. Tips for this are included in the [Live tail](/docs/feature-management-experimentation/feature-management/live-tail) article. Contact us at [support@split.io](mailto:support@split.io) if you have any issues. 
+You can view incoming impressions in [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) within a feature flag's page or the Data hub, and export them based on query criteria in the Data hub's [Data export tab](/docs/feature-management-experimentation/feature-management/export-data). If you do not see impressions arriving in Harness, ensure that your SDK is installed and functioning as expected. Tips for this are included in the [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) article. Contact us at [support@split.io](mailto:support@split.io) if you have any issues. 
 
 :::important
 Harness retains impression data for 90 days. Contact the [team at Harness FME](mailto:support@split.io) to learn more.
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
index c9e1e7b3bb3..6bafc096582 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/live-tail.md
@@ -4,7 +4,7 @@ sidebar_label: Live tail
 description: ""
 sidebar_position: 19
 redirect_from:
-  - /docs/feature-management-experimentation/feature-management/live-tail
+  - /docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail
 ---
 
 The Live tail functionality within Data hub gives development teams a single place to view and query all impression and event data. With Live tail, development teams can:
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
index c386f1f3338..d8853c76456 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
@@ -39,7 +39,7 @@ To create a feature flag, do the following:
 
 ## Create a rollout plan
 
-After you have created your feature flag, you can [define treatments and set targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) for your feature flag. This is also known as creating a rollout plan.
+After you have created your feature flag, you can [define treatments and set targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting) for your feature flag. This is also known as creating a rollout plan.
 
 :::info
 When you set up an account, two environments are automatically created. For more information, see [Environments](/docs/feature-management-experimentation/management-and-administration/fme-settings/environments).
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
index 4f806bf803c..dc4eed44eef 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-rollout-plan.md
@@ -23,7 +23,7 @@ With a rollout plan, you can:
 
 ## Define treatment names and descriptions
 
-Define the [treatments](/docs/feature-management-experimentation/feature-management/edit-treatments) you want to serve your customer. ​​Every feature you release using Split has at least two treatments. By default these are **on** and **off**. You can edit the treatment's name and description as well as add additional treatments.
+Define the [treatments](/docs/feature-management-experimentation/feature-management/setup/edit-treatments) you want to serve your customer. ​​Every feature you release using Split has at least two treatments. By default these are **on** and **off**. You can edit the treatment's name and description as well as add additional treatments.
 
 To define a treatment name, do the following:
 
@@ -250,7 +250,7 @@ To select percentages, in the **Distribute treatment as follows** section, click
 
 ### Setting the default treatment
 
-Set the [default treatment](/docs/feature-management-experimentation/feature-management/set-the-default-treatment) if the feature flag is killed or the customer has been limited from exposure to the flag. The default treatment is always one of the treatments defined for any feature flag in the targeting rules. The default treatment is returned by the SDK in following scenarios:
+Set the [default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment) if the feature flag is killed or the customer has been limited from exposure to the flag. The default treatment is always one of the treatments defined for any feature flag in the targeting rules. The default treatment is returned by the SDK in following scenarios:
 
 * Does not meet any defined conditions. The default treatment is shown to customers who are excluded from the targeting rules via the limit exposure functionality.
 * The feature flag is killed. If a particular feature flag is killed, the default treatment overrides the existing targeting rules and is returned for all customers.
@@ -265,7 +265,7 @@ You can set any of the treatments in your targeting rules as a default. We recom
 
 When you use a percentage based rollout or run an experiment using percentage-based targeting, Split can automatically monitor metric thresholds and alert you if they are exceeded. The alert baseline treatment is the status-quo or safe treatment that you want to compare other treatments to when deciding whether to alert. 
 
-For more information about selecting a valid baseline treatment, see [Select the alert baseline treatment](/docs/feature-management-experimentation/feature-management/set-the-alert-baseline-treatment).
+For more information about selecting a valid baseline treatment, see [Select the alert baseline treatment](/docs/feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment).
 
 ## Saving changes to a feature flag
 
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
index dc8868b37a5..83f4af2c46b 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
@@ -48,11 +48,11 @@ The next sections guide you through configuring your feature flag definition.
 
 Treatment names are strings that are returned when you evaluate the feature flag in your code.
 
-In the [Treatments](/docs/feature-management-experimentation/feature-management/edit-treatments) area, do the following:
+In the [Treatments](/docs/feature-management-experimentation/feature-management/setup/edit-treatments) area, do the following:
 
 1. Enter the name of each treatment and optionally enter a description.
 2. Optionally, click **Add treatment** to add additional treatments.
-3. Select the [default treatment](/docs/feature-management-experimentation/feature-management/default-treatment). This treatment will be served to everyone if the feature flag is [killed](/docs/feature-management-experimentation/feature-management/use-the-kill-switch). This treatment is also served to all traffic not exposed to the feature flag (if you _Limit exposure_ when [setting up targeting](#setting-up-targeting)).
+3. Select the [default treatment](/docs/feature-management-experimentation/feature-management/default-treatment). This treatment will be served to everyone if the feature flag is [killed](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch). This treatment is also served to all traffic not exposed to the feature flag (if you _Limit exposure_ when [setting up targeting](#setting-up-targeting)).
 
 ## Setting up dynamic configuration
 
@@ -90,7 +90,7 @@ Click the **Add new individual target** button to assign a selected treatment to
 
 In the Targeting rules area, you can:
 
-* Optionally click the **Limit exposure** button to limit [the traffic exposed](/docs/feature-management-experimentation/feature-management/limiting-exposure) to your attribute based targeting rules. For example, you can limit the customers exposed to your experiment to the percentage you choose. Users who are not exposed get the default treatment.
+* Optionally click the **Limit exposure** button to limit [the traffic exposed](/docs/feature-management-experimentation/feature-management/targeting/limiting-exposure) to your attribute based targeting rules. For example, you can limit the customers exposed to your experiment to the percentage you choose. Users who are not exposed get the default treatment.
 
 * Click **Add attribute based targeting rules** to assign individual users or segments to a selected treatment or percentage distribution.
 
@@ -98,7 +98,7 @@ In the Targeting rules area, you can:
 
     * [Target with custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes) to target specific subsets of your users based on a specific attribute (e.g., location or last-login date).
 
-    * [Target with dependencies](/docs/feature-management-experimentation/feature-management/target-with-dependencies) to target users based on the treatment they received from another flag. You can use flag dependencies to create mutually exclusive experiments. 
+    * [Target with dependencies](/docs/feature-management-experimentation/feature-management/targeting/target-with-dependencies) to target users based on the treatment they received from another flag. You can use flag dependencies to create mutually exclusive experiments. 
 
     * Optionally select **Distribute treatments as follows** and define a ___percentage distribution___ to [randomly distribute](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience) users and segments between your feature flag treatments (variations) based on the percentages you decide. This is also called a _percentage rollout_, as shown below.
 
@@ -153,7 +153,7 @@ You can implement a feature toggle that switches between `on` and `off` automati
 
 ## Setting the alert baseline treatment
 
-In the [Set alert baseline treatment](http://localhost:3000/docs/feature-management-experimentation/feature-management/set-the-alert-baseline-treatment) area, select a baseline treatment that will be used for your alert policies.
+In the [Set alert baseline treatment](/docs/feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment) area, select a baseline treatment that will be used for your alert policies.
 
 :::tip[Tip: Set the alert baseline treatment to enable alerting]
 If you set the ___alert baseline treatment___ AND you have at least one targeting rule with ___percentage distribution___, then you can receive ___[feature flag alerts](/docs/feature-management-experimentation/release-monitoring/alerts/automated-alerts-and-notifications/#setting-up-feature-flag-alerting)___ and ___[metric alerts](/docs/feature-management-experimentation/experimentation/metrics/alert-policies/#create-a-metric-alert-policy)___.
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index 1b628265781..93da0803423 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -37,4 +37,4 @@ You can also use ___percentage distribution___ to randomly distribute treatments
 
 ![](../static/target-segments-percentage-distribution.png)
 
-See [Targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting#targeting-rules) for more information on feature flag targeting, percentage distribution, and rules' evaluation order.
\ No newline at end of file
+See [Targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#targeting-rules) for more information on feature flag targeting, percentage distribution, and rules' evaluation order.
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
index 850ead1029e..3f4d5e1ed6f 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
@@ -142,7 +142,7 @@ As with attributes [created in Admin settings](#creating-custom-attributes-in-ad
 
 ## Using custom attributes in feature flag targeting
 
-After you [create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag) you can **use** (and also **create**) custom attributes in your [targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting). To add an attribute-based targeting rule to a feature flag:
+After you [create a feature flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag) you can **use** (and also **create**) custom attributes in your [targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting). To add an attribute-based targeting rule to a feature flag:
 
 1. In Harness FME, on the Definition tab of a feature flag, in the Targeting rules area, click the **Add attribute based targeting rules** button. The **IF** field/dropdown menu appears.
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/dimensional-analysis.md b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/dimensional-analysis.md
index 5ac39a7a781..87c63808afa 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/dimensional-analysis.md
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/dimensional-analysis.md
@@ -19,7 +19,7 @@ To use this feature, you must enable the experimentation package. Contact [suppo
 
 Before you start, you must be sending events with event properties. For more information about sending event properties, refer to the [Events](/docs/feature-management-experimentation/release-monitoring/events/) guide. For deeper analysis of events and properties you are currently sending, go to the Data hub, and from the Live tail tab, run your query for your events to get the right event properties. 
 
-For more information on how to run a query, refer to the [Query events section](/docs/feature-management-experimentation/feature-management/live-tail/#query-events) of the Live tail guide.
+For more information on how to run a query, refer to the [Query events section](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail/#query-events) of the Live tail guide.
 
 ## How it works
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
index 788c4094ef2..92aef795a8e 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
@@ -106,5 +106,5 @@ Examine the sample data that has come into the experiment, and consider why keys
 
 #### Useful references
 * [Attribution and exclusion](/docs/feature-management-experimentation/experimentation/experiment-results/analyzing-experiment-results/attribution-and-exclusion/#potential-complications) (see Exclusions)
-* [Reallocate](/docs/feature-management-experimentation/feature-management/reallocate-traffic)
+* [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic)
 * [Export data](/docs/feature-management-experimentation/feature-management/export-data/)
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/reallocate-traffic.md b/docs/feature-management-experimentation/60-experimentation/experiment-results/reallocate-traffic.md
index cf59c29d7b4..5e10444d0d5 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/reallocate-traffic.md
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/reallocate-traffic.md
@@ -8,7 +8,7 @@ When targeting rules are defined for a feature flag, the rules are used to assig
 In some circumstances, it is useful to reassign customers to treatments without changing the targeting rules. This process is called *reallocate* in Harness FME. Here is an example.
 
 * Your team enables a particular treatment for 10% of customers.
-* During the release, a critical issue is found and the feature flag is immediately [killed](/docs/feature-management-experimentation/feature-management/use-the-kill-switch), sending all customers to the default treatment. 
+* During the release, a critical issue is found and the feature flag is immediately [killed](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch), sending all customers to the default treatment. 
 * Your team fixes the bug and wants to release again to 10% of customers.
 * Your team does not want the same 10% to see the same treatment again, because they were exposed to the original issue. In other words, those customers were *poisoned* with regards to this treatment. 
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md b/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
index aa3c680002f..e4e54c73a1a 100644
--- a/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
+++ b/docs/feature-management-experimentation/60-experimentation/experimentation-foundations.md
@@ -67,7 +67,7 @@ See the following documentation:
 
 - [Create a Feature Flag](/docs/feature-management-experimentation/feature-management/setup/create-a-feature-flag#create-a-feature-flag)
 - [Create a Metric](/docs/feature-management-experimentation/release-monitoring/metrics/setup/#create-a-metric)
-- [Edit Treatments](/docs/feature-management-experimentation/feature-management/edit-treatments/)
+- [Edit Treatments](/docs/feature-management-experimentation/feature-management/setup/edit-treatments)
 - [Add Targeting Rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#targeting-rules)
 - [Adjust Experiment Settings](/docs/feature-management-experimentation/experimentation/setup/experiment-settings)
 
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/a-a-testing.md b/docs/feature-management-experimentation/60-experimentation/setup/a-a-testing.md
index ce9ba7a481d..9d8c1eba96d 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/a-a-testing.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/a-a-testing.md
@@ -30,7 +30,7 @@ Running an A/A test is simple. First, decide for which traffic type you wish to
 
 #### Step 2: Create a new feature flag
 
-Create the feature flag with [targeting rules](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting) serving an equally distributed percentage-based rollout. For an A/A test, this would be 50/50.
+Create the feature flag with [targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting) serving an equally distributed percentage-based rollout. For an A/A test, this would be 50/50.
 
 The names of the treatments are inconsequential, so using the defaults of on and off is fine. You could also use an alternative like A1 and A2 to reinforce that the treatments for the A/A test should be identical.
 
@@ -110,7 +110,7 @@ If you need more support investigating your A/A test results, you can [file a su
 
 **Do I need to run an A/A test before every A/B test?**
 
-No, you do not need to run an A/A test before or with every experiment. Typically, you will want to run your A/A test between setup and execution of your first experiments. If you wish to set up a global holdout or holdback group for experiments, you can use [FME dependencies](/docs/feature-management-experimentation/feature-management/target-with-dependencies/).
+No, you do not need to run an A/A test before or with every experiment. Typically, you will want to run your A/A test between setup and execution of your first experiments. If you wish to set up a global holdout or holdback group for experiments, you can use [FME dependencies](/docs/feature-management-experimentation/feature-management/targeting/target-with-dependencies).
 
 An A/A test only needs to be run once per metric and per traffic type. As mentioned above, it’s a good practice to have a separate A/A test for every traffic type being used for experimentation, since randomization, event reporting, and available metrics differ for each.
 
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
index a3cc153e428..4323d69f2ec 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
@@ -95,7 +95,7 @@ To create a dependent (child) feature flag:
 
 In your source code, you should directly evaluate these dependent feature flags. Your source code should be agnostic to the parent feature flag (that has the sole purpose of dividing traffic among the dependent flags). 
 
-For more information about dependent feature flags, see the [Target with dependencies](/docs/feature-management-experimentation/feature-management/target-with-dependencies/) help page.
+For more information about dependent feature flags, see [Target with dependencies](/docs/feature-management-experimentation/feature-management/targeting/target-with-dependencies).
 
 ## Viewing mutually exclusive experiment results
 
diff --git a/docs/feature-management-experimentation/shared/alert-policies/index.mdx b/docs/feature-management-experimentation/shared/alert-policies/index.mdx
index 72044f962bc..f8334db2d3b 100644
--- a/docs/feature-management-experimentation/shared/alert-policies/index.mdx
+++ b/docs/feature-management-experimentation/shared/alert-policies/index.mdx
@@ -71,7 +71,7 @@ Please either kill the feature flag or dismiss this alert in the Split user inte
 You are receiving this email because you were set as one of the people to be notified on this Alert Policy: (name and link to Alert Policy). If you don't want to receive these emails, please remove yourself from the list of people to be notified on the Alert policy page."
 ```
 
-The alert notification email contains information about the specific feature flag version, rule, and treatment (in comparison with the [baseline treatment](/docs/feature-management-experimentation/feature-management/set-the-alert-baseline-treatment/)) that triggered the alert. In the Split UI, you can explore the results further on the Metrics impact page.
+The alert notification email contains information about the specific feature flag version, rule, and treatment (in comparison with the [baseline treatment](/docs/feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment)) that triggered the alert. In the Split UI, you can explore the results further on the Metrics impact page.
 
 ## Metric alert summary in Split UI
 
diff --git a/docs/feature-management-experimentation/shared/events/index.mdx b/docs/feature-management-experimentation/shared/events/index.mdx
index 0156247ef8c..b9cfdab662d 100644
--- a/docs/feature-management-experimentation/shared/events/index.mdx
+++ b/docs/feature-management-experimentation/shared/events/index.mdx
@@ -41,7 +41,7 @@ Below is a sample event `checkout.click` showing the event properties with Harne
 
 <pre>\{<br />  "eventTypeId": "checkout.click",<br />  "environmentName": "Production",<br />  "trafficTypeName": "user",<br />  "timestamp": 1557936519990,<br />  "value": 0,<br />  "source": "Segment",<br />  "foreignId": "ajs-85cb03f2f34af1426d9a2128146ab832",<br />  <strong>"properties": \{ <br />    "category": "guest",<br />    "packaging_fees": 25.08,<br />    "discount_saving": 4,<br />    "label": "necklace",<br />    "login_id": null,<br />    "page_version": "unit_page",<br />    "total_tax": 32.00,<br />    "total_cost": 945.69,<br />    "type": "button",<br />    "package": "luxury",<br />    "visitor_id": null<br />  \} </strong><br />}<br /></pre>
 
-You can view the event properties being sent for a given event type using the [Live tail functionality in the Data Hub](/docs/feature-management-experimentation/feature-management/live-tail).
+You can view the event properties being sent for a given event type using the [Live tail functionality in the Data Hub](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail).
 
 These event properties are also exposed in the metric definition builder so you can create more granular and flexible metrics. Event properties can be used as filters for your [metric definition](/docs/feature-management-experimentation/release-monitoring/metrics/), or as values to be measured.
 
@@ -63,4 +63,4 @@ Harness FME supports multiple ways to explore your event data, allowing you to q
 
    <img src={dataHub} alt="Applying Metric Filters" width="700" />
 
-3. In the Data Hub, [Live tail](/docs/feature-management-experimentation/feature-management/live-tail) enables you to run a live query of impressions and events, so you can understand your event stream in more detail, including which event properties are being sent with each event type.
\ No newline at end of file
+3. In the Data Hub, [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) enables you to run a live query of impressions and events, so you can understand your event stream in more detail, including which event properties are being sent with each event type.
\ No newline at end of file

From f2908f555a2b76fd51e6b8ecaba90b7b4633d8a3 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Wed, 13 Aug 2025 12:13:48 -0700
Subject: [PATCH 05/11] Fix More Links

---
 .../40-feature-management/index.md                          | 2 +-
 .../40-feature-management/manage-flags/index.md             | 4 ++--
 .../manage-flags/maintaining-consistent-treatments.md       | 2 +-
 .../monitoring-analysis/impressions.md                      | 6 +++---
 .../setup/define-feature-flag-treatments-and-targeting.md   | 4 ++--
 .../40-feature-management/targeting/target-segments.md      | 2 +-
 .../50-release-monitoring/alerts/index.md                   | 4 ++--
 .../analyzing-experiment-results/health-check.mdx           | 4 ++--
 .../randomization-and-stickiness.md                         | 2 +-
 .../setup/mutually-exclusive-experiments.md                 | 2 +-
 10 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/docs/feature-management-experimentation/40-feature-management/index.md b/docs/feature-management-experimentation/40-feature-management/index.md
index a37232a6a02..ee8bfbebc37 100644
--- a/docs/feature-management-experimentation/40-feature-management/index.md
+++ b/docs/feature-management-experimentation/40-feature-management/index.md
@@ -75,7 +75,7 @@ The order in which treatments are defined can affect the consistency of user exp
 
 - **User-level targeting:** Use UUIDs (e.g., logged-in user IDs) for consistent user experiences.
 - **Anonymous users:** Use cookies, session IDs, or device IDs, but note that users might not receive consistent experiences if switching devices or clearing cookies.
-- **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/targeting-an-account).
+- **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/manage-flags/targeting-an-account).
 
 ### Important Consideration for Account Targeting
 
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
index 75c955af553..4c86dbc116f 100644
--- a/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/index.md
@@ -13,8 +13,8 @@ Also, explore account-level targeting and how to view and maintain consistent us
 - [Using essential scheduling](/docs/feature-management-experimentation/feature-management/manage-flags/using-essential-scheduling)
 - [Set the alert baseline treatment](/docs/feature-management-experimentation/feature-management/manage-flags/set-the-alert-baseline-treatment)
 - [Use the kill switch](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch)
-- [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate)
+- [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic)
 - [Using flag sets to boost SDK performance](/docs/feature-management-experimentation/feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance)
-- [Targeting an Account or Organization](/docs/feature-management-experimentation/feature-management/manage-flags/targeting-an-account-or-organization)
+- [Targeting an Account or Organization](/docs/feature-management-experimentation/feature-management/manage-flags/targeting-an-account)
 - [Viewing User Treatments](/docs/feature-management-experimentation/feature-management/manage-flags/viewing-user-treatments)
 - [Maintaining Consistent Treatments](/docs/feature-management-experimentation/feature-management/manage-flags/maintaining-consistent-treatments)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md b/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
index 07f1faa006f..54a3c253fec 100644
--- a/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
+++ b/docs/feature-management-experimentation/40-feature-management/manage-flags/maintaining-consistent-treatments.md
@@ -18,7 +18,7 @@ The goal is to maintain state so that a user is always exposed to the same treat
 
 Split is designed for imperceptibly fast treatment computation. For example, in Java, treatments are computed in just a few hundred microseconds. To achieve this speed, Split does **not** retain state. After serving a treatment, it does not “remember” what was served.
 
-Instead, Split uses a deterministic hashing algorithm to assign treatments. This ensures a [“sticky” experience](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience/) where, as long as the feature flag targeting definition remains unchanged, users receive the **same** treatment every time.
+Instead, Split uses a deterministic hashing algorithm to assign treatments. This ensures a [“sticky” experience](/docs/feature-management-experimentation/feature-management) where, as long as the feature flag targeting definition remains unchanged, users receive the **same** treatment every time.
 
 However, if the targeting rules or percentages change, some users may receive different treatments. This behavior is intentional and appropriate for most use cases, but not all.
 
diff --git a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
index 8a1229daa45..44ee9169f1d 100644
--- a/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
+++ b/docs/feature-management-experimentation/40-feature-management/monitoring-analysis/impressions.md
@@ -125,7 +125,7 @@ Use the following tables to verify that your SDK or customer-deployed component
 
 ## Viewing impressions in Harness
 
-You can view incoming impressions in [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) within a feature flag's page or the Data hub, and export them based on query criteria in the Data hub's [Data export tab](/docs/feature-management-experimentation/feature-management/export-data). If you do not see impressions arriving in Harness, ensure that your SDK is installed and functioning as expected. Tips for this are included in the [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) article. Contact us at [support@split.io](mailto:support@split.io) if you have any issues. 
+You can view incoming impressions in [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) within a feature flag's page or the Data hub, and export them based on query criteria in the Data hub's [Data export tab](/docs/feature-management-experimentation/feature-management/monitoring-analysis/export-data). If you do not see impressions arriving in Harness, ensure that your SDK is installed and functioning as expected. Tips for this are included in the [Live tail](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail) article. Contact us at [support@split.io](mailto:support@split.io) if you have any issues. 
 
 :::important
 Harness retains impression data for 90 days. Contact the [team at Harness FME](mailto:support@split.io) to learn more.
@@ -151,8 +151,8 @@ Usually, a treatment is served because the customer matched a particular rule. I
  
 | **Special case** | **Treatment served** | **Targeting rule label** |
 | --- | --- | --- |
-| Feature flag was killed | [Default](/docs/feature-management-experimentation/feature-management/default-treatment) | `killed` |
-| No rule matched | [Default](/docs/feature-management-experimentation/feature-management/default-treatment) | `no rule matched` |
+| Feature flag was killed | [Default](/docs/feature-management-experimentation/feature-management/setup/default-treatment) | `killed` |
+| No rule matched | [Default](/docs/feature-management-experimentation/feature-management/setup/default-treatment) | `no rule matched` |
 | Feature flag was not found, for example, it may not have been downloaded by the SDK yet | [Control](/docs/feature-management-experimentation/feature-management/setup/control-treatment) | `definition not found` |
 | There was an exception while evaluating treatment | [Control](/docs/feature-management-experimentation/feature-management/setup/control-treatment) | `exception`  |
 
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
index 83f4af2c46b..7f2d9b0a84c 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targeting.md
@@ -52,7 +52,7 @@ In the [Treatments](/docs/feature-management-experimentation/feature-management/
 
 1. Enter the name of each treatment and optionally enter a description.
 2. Optionally, click **Add treatment** to add additional treatments.
-3. Select the [default treatment](/docs/feature-management-experimentation/feature-management/default-treatment). This treatment will be served to everyone if the feature flag is [killed](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch). This treatment is also served to all traffic not exposed to the feature flag (if you _Limit exposure_ when [setting up targeting](#setting-up-targeting)).
+3. Select the [default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment). This treatment will be served to everyone if the feature flag is [killed](/docs/feature-management-experimentation/feature-management/manage-flags/use-the-kill-switch). This treatment is also served to all traffic not exposed to the feature flag (if you _Limit exposure_ when [setting up targeting](#setting-up-targeting)).
 
 ## Setting up dynamic configuration
 
@@ -100,7 +100,7 @@ In the Targeting rules area, you can:
 
     * [Target with dependencies](/docs/feature-management-experimentation/feature-management/targeting/target-with-dependencies) to target users based on the treatment they received from another flag. You can use flag dependencies to create mutually exclusive experiments. 
 
-    * Optionally select **Distribute treatments as follows** and define a ___percentage distribution___ to [randomly distribute](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience) users and segments between your feature flag treatments (variations) based on the percentages you decide. This is also called a _percentage rollout_, as shown below.
+    * Optionally select **Distribute treatments as follows** and define a ___percentage distribution___ to [randomly distribute](/docs/feature-management-experimentation/feature-management) users and segments between your feature flag treatments (variations) based on the percentages you decide. This is also called a _percentage rollout_, as shown below.
 
    ![](../static/define-feature-flag-treatments-and-targeting-attribute-based.png)
 
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index 93da0803423..f87396d4bcc 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -5,7 +5,7 @@ description: ""
 sidebar_position: 7
 ---
 
-Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/40-feature-management/define-feature-flag-treatments-and-targeting.md#setting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
+Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targetingsetting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
 
 [Segments](/docs/feature-management-experimentation/feature-management/targeting/segments) are lists of user IDs. On your feature flag Definition tab, you can create targeting rules that assign treatments to segments.
 
diff --git a/docs/feature-management-experimentation/50-release-monitoring/alerts/index.md b/docs/feature-management-experimentation/50-release-monitoring/alerts/index.md
index 74e0289e4a4..2c78118fa36 100644
--- a/docs/feature-management-experimentation/50-release-monitoring/alerts/index.md
+++ b/docs/feature-management-experimentation/50-release-monitoring/alerts/index.md
@@ -45,11 +45,11 @@ Compare metrics against a [baseline treatment](.././alerts/set-the-alert-baselin
 
 ## Manage alerts
 
-When an alert fires, you can access the [alert details](./view-triggered-alerts.md) and kill the flag directly from the flag page to stop traffic.
+When an alert fires, you can access the [alert details](./view-triggered-alerts) and kill the flag directly from the flag page to stop traffic.
 
 | **Action** | **Description** |
 | ---- | ---- | 
-| Kill feature flag | If you decide to kill a feature flag due to an alert, the [default treatment](/docs/feature-management-experimentation/feature-management/default-treatment/) overrides the existing targeting rules and is returned for all customers. |
+| Kill feature flag | If you decide to kill a feature flag due to an alert, the [default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment) overrides the existing targeting rules and is returned for all customers. |
 
 ### Troubleshooting alerts
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
index 92aef795a8e..ace71a826fc 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
@@ -106,5 +106,5 @@ Examine the sample data that has come into the experiment, and consider why keys
 
 #### Useful references
 * [Attribution and exclusion](/docs/feature-management-experimentation/experimentation/experiment-results/analyzing-experiment-results/attribution-and-exclusion/#potential-complications) (see Exclusions)
-* [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic)
-* [Export data](/docs/feature-management-experimentation/feature-management/export-data/)
+* [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic-traffic)
+* [Export data](/docs/feature-management-experimentation/feature-management/monitoring-analysis/export-data/)
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/viewing-experiment-results/randomization-and-stickiness.md b/docs/feature-management-experimentation/60-experimentation/experiment-results/viewing-experiment-results/randomization-and-stickiness.md
index a5263d690d8..9ec2856026f 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/viewing-experiment-results/randomization-and-stickiness.md
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/viewing-experiment-results/randomization-and-stickiness.md
@@ -21,7 +21,7 @@ For feature flags that use traffic allocation or percentage rollouts, FME uses a
 
 Of course, if you change the feature flag's targeting rules, some users may be moved from one treatment to another. This could happen if they fall under a different rule or if the percentages in a rule are changed. That is the intent for an overwhelming number of use cases but is not ideal in some scenarios.
 
-More information on how Harness FME deterministic hashing and targeting engine work can be found [here](https://help.split.io/hc/en-us/articles/360043397251-Split-and-consistent-assignment-of-treatments) and [here](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience).
+More information on how Harness FME deterministic hashing and targeting engine work can be found [here](https://help.split.io/hc/en-us/articles/360043397251-Split-and-consistent-assignment-of-treatments) and [here](/docs/feature-management-experimentation/feature-management).
 
 ## Experiment implications
 
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
index 4323d69f2ec..07db7a80380 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/mutually-exclusive-experiments.md
@@ -107,7 +107,7 @@ Mutually exclusive experiments carry some inherent limitations. You should be aw
 
 ### Ending an experiment and reallocating users
 
-The Harness FME feature flag (parent flag) that divides traffic between mutually exclusive experiments uses a random and deterministic hashing algorithm to allocate traffic. Suppose you want to end one of the concurrent mutually exclusive experiments and reallocate the users to the other experiments (and you are sure this will not invalidate your results). To learn how your traffic would be reallocated, see [How does Harness FME ensure a consistent user experience?](/docs/feature-management-experimentation/feature-management/faqs/ensure-a-consistent-user-experience). You may also consider using [dynamic configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations/) in your flag definitions if you anticipate ending a mutually exclusive experiment early.
+The Harness FME feature flag (parent flag) that divides traffic between mutually exclusive experiments uses a random and deterministic hashing algorithm to allocate traffic. Suppose you want to end one of the concurrent mutually exclusive experiments and reallocate the users to the other experiments (and you are sure this will not invalidate your results). To learn how your traffic would be reallocated, see [How does Harness FME ensure a consistent user experience?](/docs/feature-management-experimentation/feature-management). You may also consider using [dynamic configuration](/docs/feature-management-experimentation/feature-management/setup/dynamic-configurations/) in your flag definitions if you anticipate ending a mutually exclusive experiment early.
 
 :::note 
 We generally do not recommend re-using the divisions created by the parent feature flag for new experiments, because you would be experimenting with the subset of your user population that was exposed to an earlier experiment. This subset may not be representative of your entire user population.

From 4f837fdb2dca928b02c780b6912ad4d07826d895 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Wed, 13 Aug 2025 12:33:20 -0700
Subject: [PATCH 06/11] Fix Links in Release Notes

---
 .../targeting/target-segments.md              |  2 +-
 .../health-check.mdx                          |  2 +-
 .../feature-management-experimentation.md     | 32 +++++++++----------
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index f87396d4bcc..e8f0f6646f4 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -5,7 +5,7 @@ description: ""
 sidebar_position: 7
 ---
 
-Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/40-feature-management/setup/define-feature-flag-treatments-and-targetingsetting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
+Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targetingsetting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
 
 [Segments](/docs/feature-management-experimentation/feature-management/targeting/segments) are lists of user IDs. On your feature flag Definition tab, you can create targeting rules that assign treatments to segments.
 
diff --git a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
index ace71a826fc..f56cfc706f4 100644
--- a/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
+++ b/docs/feature-management-experimentation/60-experimentation/experiment-results/analyzing-experiment-results/health-check.mdx
@@ -106,5 +106,5 @@ Examine the sample data that has come into the experiment, and consider why keys
 
 #### Useful references
 * [Attribution and exclusion](/docs/feature-management-experimentation/experimentation/experiment-results/analyzing-experiment-results/attribution-and-exclusion/#potential-complications) (see Exclusions)
-* [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic-traffic)
+* [Reallocate](/docs/feature-management-experimentation/feature-management/manage-flags/reallocate-traffic)
 * [Export data](/docs/feature-management-experimentation/feature-management/monitoring-analysis/export-data/)
diff --git a/release-notes/feature-management-experimentation.md b/release-notes/feature-management-experimentation.md
index 96bb1d6c14c..32d4b7998a1 100644
--- a/release-notes/feature-management-experimentation.md
+++ b/release-notes/feature-management-experimentation.md
@@ -64,8 +64,8 @@ This feature is especially useful when working with large user segments, as they
 
 #### Related documentation
 
-- [Create a segment](/docs/feature-management-experimentation/feature-management/segments/)
-- [Target segments](/docs/feature-management-experimentation/feature-management/target-segments/)
+- [Create a segment](/docs/feature-management-experimentation/feature-management/targeting/segments/)
+- [Target segments](/docs/feature-management-experimentation/feature-management/targeting/target-segments/)
 - [Android SDK](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/android-sdk)
 - [Browser SDK](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/browser-sdk)
 - [Browser Suite](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-suites/browser-suite)
@@ -195,7 +195,7 @@ This feature allows you to streamline impressions sent to third party integratio
 ![Impression tracking toggle](./static/fme/impression-tracking-toggle.png)
 
 #### Related documentation
-- [Tracking impressions](/docs/feature-management-experimentation/feature-management/impressions/#tracking-impressions)
+- [Tracking impressions](/docs/feature-management-experimentation/feature-management/targeting/impressions/#tracking-impressions)
 
 ### [New Feature] AI settings
 ----
@@ -264,7 +264,7 @@ Effective immediately, Enterprise tier customers may create and use Large segmen
 
 ![Image](./static/fme/image-53.png)
 Learn more about Large segments and the ways they differ from Standard segments in the documentation:
-- [Create a segment](/docs/feature-management-experimentation/feature-management/segments)
+- [Create a segment](/docs/feature-management-experimentation/feature-management/targeting/segments)
 - [Target segments](/docs/feature-management-experimentation/feature-management/target-segments)
 
 Note: The initial release of Large segments is focused on client-side SDK usage only. Server-side SDKs do not yet support Large segments, but soon will. Until they are supported, evaluations of feature flags that target Large segments will return control on server-side SDKs.
@@ -273,13 +273,13 @@ Note: The initial release of Large segments is focused on client-side SDK usage
 After familiarizing yourself with Large segments at the above links, you may find these UI and API equivalent documentation links handy for automating the steps via the Admin API:
 ###### Steps for creating and populating a Large segment using either UI or API
 1. Create a Large segment (just **metadata**, no Environment definition)
-- [UI steps](/docs/feature-management-experimentation/feature-management/segments#creating-a-segment) (select Large)
+- [UI steps](/docs/feature-management-experimentation/feature-management/targeting/segments#creating-a-segment) (select Large)
 - [API steps](https://docs.split.io/reference/createlargesegment)
 2. Create a **definition** for a Large segment in an Environment (no user IDs)
-- [UI steps](/docs/feature-management-experimentation/feature-management/segments#adding-user-ids-to-a-segment) (step 3)
+- [UI steps](/docs/feature-management-experimentation/feature-management/targeting/segments#adding-user-ids-to-a-segment) (step 3)
 - [API steps](https://docs.split.io/reference/createlargesegmentinenvironment)
 3. Add **user IDs** to a Large segment (to the definition created in step 2)
-- [UI steps](/docs/feature-management-experimentation/feature-management/segments#file-import-for-large-segments)
+- [UI steps](/docs/feature-management-experimentation/feature-management/targeting/segments#file-import-for-large-segments)
 - [API steps](https://docs.split.io/reference/create-change-request#open-change-request-to-add-members-to-a-large-segment)
 
 ###### Adding an approval step via Admin API
@@ -318,8 +318,8 @@ If "is in list" is chosen as the matcher type, suggested values are shown:
 ![Image](./static/fme/3-choose-a-suggsted-value-1920x890.png)
 
 ###### **Related Documentation:**
-- [Creating individual custom attributes in Admin settings](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#creating-individual-custom-attributes-in-admin-settings)
-- [Creating multiple custom attributes in Admin Settings](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#creating-multiple-custom-attributes-in-admin-settings) (CSV upload)
+- [Creating individual custom attributes in Admin settings](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#creating-individual-custom-attributes-in-admin-settings)
+- [Creating multiple custom attributes in Admin Settings](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#creating-multiple-custom-attributes-in-admin-settings) (CSV upload)
 
 ## November 2024
 ### 2024-11-27
@@ -423,7 +423,7 @@ Users can now categorize their organization’s most important metrics as  “g
 ### 2024-06-06
 #### SDK Enhancements
 ##### Semantic Versioning Targeting
-Using the latest Split SDKs, users can more easily define targeting rules for new features based on app, OS, and other versions using attribute-based targeting. The SDK then automatically serves the appropriate treatment to users without needing additional code configurations. Split’s native [Semantic Versioning Targeting](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#semver-attributes) removes the additional complexities and manual work that comes with targeting different application versions, allowing users to seamlessly deliver different experiences.
+Using the latest Split SDKs, users can more easily define targeting rules for new features based on app, OS, and other versions using attribute-based targeting. The SDK then automatically serves the appropriate treatment to users without needing additional code configurations. Split’s native [Semantic Versioning Targeting](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#semver-attributes) removes the additional complexities and manual work that comes with targeting different application versions, allowing users to seamlessly deliver different experiences.
 
 ## May 2024
 ### 2024-05-31
@@ -509,7 +509,7 @@ Dynamic Configuration’s JSON input field now supports text wraps. This allows
 ### 2023-12-12
 #### Feature Flag Management Console 
 ##### Flag Sets 
-With [Flag Sets](/docs/feature-management-experimentation/feature-management/using-flag-sets-to-boost-sdk-performance/), users can group flags that logically belong together, so that the SDK only retrieves relevant flag definitions when initialized. This reduces SDK latency, memory consumption, and CPU utilization.
+With [Flag Sets](/docs/feature-management-experimentation/feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance/), users can group flags that logically belong together, so that the SDK only retrieves relevant flag definitions when initialized. This reduces SDK latency, memory consumption, and CPU utilization.
 
 ### 2023-12-11
 #### Monitoring
@@ -660,7 +660,7 @@ Developers can now [configure specific proxies](/docs/feature-management-experim
 ### 2023-04-06
 #### Feature Management Console
 ##### Essential Scheduling
-[Essential scheduling](/docs/feature-management-experimentation/feature-management/using-essential-scheduling/) provides the capability to launch a feature on a certain date and time, up to 90 days in advance. This enables users to get all the necessary rollout work done, like getting approvals, long before the release.
+[Essential scheduling](/docs/feature-management-experimentation/feature-management/manage-flags/using-essential-scheduling/) provides the capability to launch a feature on a certain date and time, up to 90 days in advance. This enables users to get all the necessary rollout work done, like getting approvals, long before the release.
 ##### Simplified Feature Flag Configurations: Split Environment Usability Updates (Release 1)
 There are new UI and UX updates to the feature flag editing experience that make the selection of [environments](/docs/feature-management-experimentation/management-and-administration/fme-settings/environments) and the editing of flag details more intuitive and easier. The updates include an environment pick list showcasing feature flag traffic per environment, production environment indicators, and upgraded headers to easily edit flag details.
 #### Security
@@ -724,7 +724,7 @@ Users can now [manage their alert policy](/docs/feature-management-experimentati
 #### 2022-12-22
 ##### Feature Management Console
 ###### Usability Updates on the Targeting Rules page
-The usability updates on the [Targeting Rules page](/docs/feature-management-experimentation/feature-management/define-feature-flag-treatments-and-targeting/#targeting-rules) include decluttering the tab, reducing confusion on default rule, and default targeting and starting the UI upgrade journey.
+The usability updates on the [Targeting Rules page](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting/#targeting-rules) include decluttering the tab, reducing confusion on default rule, and default targeting and starting the UI upgrade journey.
 #### 2022-12-15
 ##### Integrations
 ###### Split-ServiceNow Integrations
@@ -745,7 +745,7 @@ The **Metrics impact** tab now has [redesigned metric cards](/docs/feature-manag
 #### 2022-11-07
 ##### Feature Management Console
 ###### Attribute Dictionary Iteration
-Admins can now add up to 100 suggested values when creating [custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#creating-multiple-attributes) using the `string` type. This will give users more flexibility when creating targeting rules.
+Admins can now add up to 100 suggested values when creating [custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#creating-multiple-attributes) using the `string` type. This will give users more flexibility when creating targeting rules.
 #### 2022-11-01
 ##### SDK Enhancements
 ###### Split Evaluator Update
@@ -780,11 +780,11 @@ The [Ready to clean up](https://help.split.io/hc/en-us/articles/4405016480269-Us
 #### 2022-08-19
 ##### Feature Management Console
 ###### Create Multiple Attributes
-Admins can now create [multiple custom attributes](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#creating-multiple-attributes) by uploading them using a CSV file, helping to reduce time and errors.
+Admins can now create [multiple custom attributes](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#creating-multiple-attributes) by uploading them using a CSV file, helping to reduce time and errors.
 #### 2022-08-01
 ##### Feature Management Console
 ###### Attribute Dictionary
-With [Split's Attribute Dictionary](/docs/feature-management-experimentation/feature-management/target-with-custom-attributes/#adding-an-attribute), admins can now easily create custom attributes and suggested values. Users can then select from a list of predefined attributes and values to help decrease development time.
+With [Split's Attribute Dictionary](/docs/feature-management-experimentation/feature-management/targeting/target-with-custom-attributes/#adding-an-attribute), admins can now easily create custom attributes and suggested values. Users can then select from a list of predefined attributes and values to help decrease development time.
 ##### SDK Enhancements
 ###### LogLevel Configurations
 LogLevel Configuration gives developers more granularity when choosing what level of logs they want to capture within their [i](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/ios-sdk#track)[OS](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/ios-sdk#configuration) and [Android](/docs/feature-management-experimentation/sdks-and-infrastructure/client-side-sdks/android-sdk#configuration) SDKs.

From 13c8c8fe5f864f91e44d68fddb920d73e24f1b17 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Wed, 13 Aug 2025 12:34:21 -0700
Subject: [PATCH 07/11] One More

---
 .../sdk-overview/sdk-validation-checklist.md                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/feature-management-experimentation/20-sdks-and-infrastructure/sdk-overview/sdk-validation-checklist.md b/docs/feature-management-experimentation/20-sdks-and-infrastructure/sdk-overview/sdk-validation-checklist.md
index 32a2bf88ad5..2b897f55cf3 100644
--- a/docs/feature-management-experimentation/20-sdks-and-infrastructure/sdk-overview/sdk-validation-checklist.md
+++ b/docs/feature-management-experimentation/20-sdks-and-infrastructure/sdk-overview/sdk-validation-checklist.md
@@ -39,7 +39,7 @@ The following validation considerations are relevant for all FME SDKs.
 
 * **Validate 24 hours of impressions (and events) from the Data hub.** Take a feature flag that has a known high activity and download all impressions for it from the previous 24 hour period. Ensure that the number of treatments and IDs all match with expectations. For events, take the previous full 24 hours of events, if applicable. With impressions, take note if you are seeing any ‘CONTROL’ treatments as those warrant further investigation to understand why those are happening.
 
-* **Evaluate if you can take advantage of Flag Sets**   You can use Flag Sets for limiting the flags downloaded by an SDK. [Flag Sets](/docs/feature-management-experimentation/feature-management/using-flag-sets-to-boost-sdk-performance) allow you to control from Harness FME which flags are downloaded by an SDK. This means you can ensure that only the flags needed for a frontend SDK or a backend SDK are downloaded. This reduces the time for the SDK to get ready while also saving memory and bandwidth. 
+* **Evaluate if you can take advantage of Flag Sets**   You can use Flag Sets for limiting the flags downloaded by an SDK. [Flag Sets](/docs/feature-management-experimentation/feature-management/manage-flags/using-flag-sets-to-boost-sdk-performance) allow you to control from Harness FME which flags are downloaded by an SDK. This means you can ensure that only the flags needed for a frontend SDK or a backend SDK are downloaded. This reduces the time for the SDK to get ready while also saving memory and bandwidth. 
 
 ## Browser SDKs (including Angular, React, etc.)
 

From 02b8ab082d8c39e25a64665dd709daa7b5dcc805 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Wed, 13 Aug 2025 12:56:12 -0700
Subject: [PATCH 08/11] Last Link Cleanup

---
 .../40-feature-management/targeting/target-segments.md        | 2 +-
 release-notes/feature-management-experimentation.md           | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
index e8f0f6646f4..e426e678b1b 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-segments.md
@@ -5,7 +5,7 @@ description: ""
 sidebar_position: 7
 ---
 
-Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targetingsetting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
+Once you create a feature flag, you can [create feature flag targeting rules](/docs/feature-management-experimentation/feature-management/setup/define-feature-flag-treatments-and-targeting#setting-up-targeting) that target individual user IDs (user keys), but you can also use segments.
 
 [Segments](/docs/feature-management-experimentation/feature-management/targeting/segments) are lists of user IDs. On your feature flag Definition tab, you can create targeting rules that assign treatments to segments.
 
diff --git a/release-notes/feature-management-experimentation.md b/release-notes/feature-management-experimentation.md
index 32d4b7998a1..55bfb7fe536 100644
--- a/release-notes/feature-management-experimentation.md
+++ b/release-notes/feature-management-experimentation.md
@@ -195,7 +195,7 @@ This feature allows you to streamline impressions sent to third party integratio
 ![Impression tracking toggle](./static/fme/impression-tracking-toggle.png)
 
 #### Related documentation
-- [Tracking impressions](/docs/feature-management-experimentation/feature-management/targeting/impressions/#tracking-impressions)
+- [Tracking impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions/#tracking-impressions)
 
 ### [New Feature] AI settings
 ----
@@ -265,7 +265,7 @@ Effective immediately, Enterprise tier customers may create and use Large segmen
 ![Image](./static/fme/image-53.png)
 Learn more about Large segments and the ways they differ from Standard segments in the documentation:
 - [Create a segment](/docs/feature-management-experimentation/feature-management/targeting/segments)
-- [Target segments](/docs/feature-management-experimentation/feature-management/target-segments)
+- [Target segments](/docs/feature-management-experimentation/feature-management/targeting/target-segments)
 
 Note: The initial release of Large segments is focused on client-side SDK usage only. Server-side SDKs do not yet support Large segments, but soon will. Until they are supported, evaluations of feature flags that target Large segments will return control on server-side SDKs.
 

From 1adf8828ab334ef29147d7e92d35ec8a43d8101f Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Thu, 14 Aug 2025 11:05:06 -0700
Subject: [PATCH 09/11] FMEPRD-222

---
 .../overview/create-an-experiment.md          |  8 +++++++-
 .../10-getting-started/overview/index.md      | 20 +++++++++++--------
 .../40-feature-management/index.md            | 11 +++++++++-
 .../setup/create-a-feature-flag.md            |  9 ++++++++-
 .../setup/experiment-scope.md                 |  8 +++++++-
 5 files changed, 44 insertions(+), 12 deletions(-)

diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
index 84ab0511036..043773a8509 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
@@ -64,4 +64,10 @@ You can assign the following metric categories to your experiment any time after
 * Guardrail metrics, which ensure important areas like performance or stability are not negatively impacted.
 * Supporting metrics, which offer additional context, even if they’re not directly related to the experiment’s success criteria.
 
-For more information, see the [Metrics documentation](/docs/feature-management-experimentation/experimentation/metrics/).
\ No newline at end of file
+For more information, see the [Metrics documentation](/docs/feature-management-experimentation/experimentation/metrics/).
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [A/B Testing For Feature Flags, What It Is And What It Shouldn’t Be](https://www.harness.io/blog/a-b-testing-for-feature-flags-what-it-is-and-what-it-shouldnt-be)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/index.md b/docs/feature-management-experimentation/10-getting-started/overview/index.md
index 3e64a686736..744871c54b7 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/index.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/index.md
@@ -184,7 +184,13 @@ Use traffic types to easily identify the customer traffic you are splitting. A t
 
 ### Testing with feature flags
 
-Feature flags can be integrated into a variety of testing strategies across the software development lifecycle. In quality assurance and engineering workflows, they support unit tests, end-to-end tests, smoke tests, lifecycle testing, A/B testing, and both shift-left and shift-right practices. Teams can also use localhost mode to validate flag behavior in isolation or as part of CI pipelines. 
+Feature flags can be integrated into a variety of testing strategies across the software development lifecycle. In quality assurance and engineering workflows, they support unit, smoke, regression, sanity, and end-to-end testing, along with lifecycle testing, usability testing (moderated, unmoderated, think-aloud, remote), A/B testing, benchmark testing, and accessibility checks. 
+
+* **[Smoke testing](https://www.harness.io/blog/differences-between-smoke-testing-and-sanity-testing)**: A quick validation that critical functionality works before deeper testing begins.
+* **[Regression testing](https://www.harness.io/blog/comparing-smoke-tests-to-regression-tests)**: Ensures recent changes haven’t broken existing functionality.
+* **[Sanity testing](https://www.harness.io/blog/differences-between-smoke-testing-and-sanity-testing)**: A fast check that recent fixes or updates didn’t disrupt core features.
+
+Teams can also use localhost mode to validate flag behavior in isolation or as part of CI pipelines, and apply flags in both shift-left and shift-right practices. 
 
 For guidance on incorporating feature flags into these workflows, see [Creating Unit Tests for Code Using FME SDKs](/docs/feature-management-experimentation/api/unit-tests) and [Using Test Automation in QA](/docs/feature-management-experimentation/api/test-automation).
 
@@ -236,12 +242,10 @@ This list may change in the event of a failover, so we recommend subscribing to
 
 Split uses a third-party provider, SendGrid, to send user invitations. If your spam blocker prevents these from being delivered, allow the following IP address: `168.245.9.60`.
 
-### Additional topics
-
-* Integrate & automate: Understand Split’s API and connect Split with the solutions you use today.
-
-* Working as a team: Functionality for teams that need more controls and additional organization.
+## Further Reading
 
-* Securing your data: Carry your security best practices through into Split.
+Additional documentation, blog blog links, and articles:
 
-* Support: Understand support definitions and incident response times.
\ No newline at end of file
+- [Testing Redesigned Data Pipelines with Split](https://www.harness.io/blog/testing-pipelines-split)
+- [Understanding Different Types of Usability Testing](https://www.harness.io/blog/types-of-usability-testing)
+- [Differences Between Smoke Testing and Sanity Testing](https://www.harness.io/blog/differences-between-smoke-testing-and-sanity-testing#feature-flags-and-their-role-in-testing)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/index.md b/docs/feature-management-experimentation/40-feature-management/index.md
index ee8bfbebc37..0cbe761678e 100644
--- a/docs/feature-management-experimentation/40-feature-management/index.md
+++ b/docs/feature-management-experimentation/40-feature-management/index.md
@@ -77,8 +77,17 @@ The order in which treatments are defined can affect the consistency of user exp
 - **Anonymous users:** Use cookies, session IDs, or device IDs, but note that users might not receive consistent experiences if switching devices or clearing cookies.
 - **Account-level targeting:** Target by account ID so [all users in the same account get the same experience](/docs/feature-management-experimentation/feature-management/manage-flags/targeting-an-account).
 
-### Important Consideration for Account Targeting
+### Account Targeting
 
 If you do a 50/50 split by account, large accounts with many users might unevenly skew the user count on one side of the experiment, potentially leading to an actual end-user distribution higher than 50%.
 
 Harness FME ensures a consistent user experience by combining deterministic hashing with seed-based bucketing and configurable limit exposure, enabling reliable and predictable feature rollouts across different users and environments.
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [Testing AI Models Using Split](https://www.harness.io/blog/testing-ai-models-using-split)
+- [Test-Driven or Feature Flag-Driven Development: What’s Best for Your Team?](https://www.harness.io/blog/test-driven-or-feature-flag-driven-development-whats-best-for-your-team)
+- [5 Best Practices for Testing in Production with Feature Flags](https://www.harness.io/blog/feature-flags-test-in-production)
+- [What Is Testing in Production & How to Avoid The Risks](https://www.harness.io/blog/testing-in-production)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
index d8853c76456..d4b2e537c8f 100644
--- a/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/40-feature-management/setup/create-a-feature-flag.md
@@ -109,4 +109,11 @@ If your feature flag uses random percentage-based targeting, user bucketing will
 
 Rather than migrating flags, Harness recommends setting up workspaces in advance and having teams use their designated workspace for all net-new releases. 
 
-Existing feature flags should remain in the current (often “Default”) workspace until they are fully removed from code. This strategy avoids disruption and allows teams to incrementally adopt their own workspaces without impacting production traffic.
\ No newline at end of file
+Existing feature flags should remain in the current (often “Default”) workspace until they are fully removed from code. This strategy avoids disruption and allows teams to incrementally adopt their own workspaces without impacting production traffic.
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [Parity Testing With Feature Flags](https://www.harness.io/blog/parity-testing-with-feature-flags)
+- [Comparing Smoke Tests to Regression Tests](https://www.harness.io/blog/comparing-smoke-tests-to-regression-tests)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/60-experimentation/setup/experiment-scope.md b/docs/feature-management-experimentation/60-experimentation/setup/experiment-scope.md
index 9055f571ff5..d4b71818968 100644
--- a/docs/feature-management-experimentation/60-experimentation/setup/experiment-scope.md
+++ b/docs/feature-management-experimentation/60-experimentation/setup/experiment-scope.md
@@ -51,4 +51,10 @@ This approach has a number of advantages for our customers:
 
 * Uniform experience across devices, versions and viewports: Since the web app can query the same service, our customers can be confident that their users will see a feature as on or off, regardless of whether they access the product via mobile or web. Thus, calling home is portable.
 * Update your platform without touching the app: Split is a core piece of our customers’ infrastructure and is always improving. By hosting Split on the server-side, our customers can confidently upgrade their server-side Split library without worrying about older, possibly conflicting, versions of Split being used in older mobile apps. Thus, calling home avoids versioning headaches.
-* No impact to File Size: By hosting the library on the server side, our customers need never worry about increasing the footprint of their mobile app by adding Split’s library. Thus, calling home is safe.
\ No newline at end of file
+* No impact to File Size: By hosting the library on the server side, our customers need never worry about increasing the footprint of their mobile app by adding Split’s library. Thus, calling home is safe.
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [Don’t Be Left In the Dark: Try Trigger Testing](https://www.harness.io/blog/trigger-testing-with-lights-on)
\ No newline at end of file

From 2246011f513b3645aad3c73391f91368ab5bb471 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Wed, 20 Aug 2025 09:34:16 -0700
Subject: [PATCH 10/11] Fix Link

---
 .../shared/alert-policies/index.mdx                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/feature-management-experimentation/shared/alert-policies/index.mdx b/docs/feature-management-experimentation/shared/alert-policies/index.mdx
index e35ab08b901..3a9d151b916 100644
--- a/docs/feature-management-experimentation/shared/alert-policies/index.mdx
+++ b/docs/feature-management-experimentation/shared/alert-policies/index.mdx
@@ -98,7 +98,7 @@ For each alert, you can take the following actions:
 
 | Action | Description |
 |---|---|
-| Kill feature flag | If you decide to kill a feature flag due to an alert, the [default treatment](/docs/feature-management-experimentation/feature-management/default-treatment/) overrides the existing targeting rules and is returned for all customers. |
+| Kill feature flag | If you decide to kill a feature flag due to an alert, the [default treatment](/docs/feature-management-experimentation/feature-management/setup/default-treatment) overrides the existing targeting rules and is returned for all customers. |
 
 ## Troubleshooting metric alerts
 

From 4a8e7d1167e125e9024034a2f799d599f4009844 Mon Sep 17 00:00:00 2001
From: Austin Lai <austin.lai@harness.io>
Date: Thu, 21 Aug 2025 17:58:20 -0700
Subject: [PATCH 11/11] SME Review

---
 .../overview/create-a-feature-flag.md         |  8 ++++-
 .../overview/create-an-experiment.md          |  8 +----
 .../40-feature-management/index.md            | 32 +++++++++----------
 .../target-with-custom-attributes.md          |  8 +++--
 .../release-agent/index.md                    |  8 ++++-
 5 files changed, 36 insertions(+), 28 deletions(-)

diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
index 2007b5745dd..a83b7858b0e 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-a-feature-flag.md
@@ -60,4 +60,10 @@ if (treatment === 'on') {
 
 To help verify that treatments are being served to your users, click the [Live tail tab](/docs/feature-management-experimentation/feature-management/monitoring-analysis/live-tail/) to view a stream of impressions or SDK evaluations. Impressions occur whenever a visitor is assigned a treatment (i.e., variations) for a feature flag. 
 
-These impressions are generated by the SDKs each time `getTreatment` is called. They are periodically sent back to Split's servers where they are stored and can be accessed for later use. For more information about impressions, see [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions).
\ No newline at end of file
+These impressions are generated by the SDKs each time `getTreatment` is called. They are periodically sent back to Split's servers where they are stored and can be accessed for later use. For more information about impressions, see [Impressions](/docs/feature-management-experimentation/feature-management/monitoring-analysis/impressions).
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [Feature Flags For Dummies](https://www.harness.io/resources/feature-flags-for-dummies)
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
index 043773a8509..84ab0511036 100644
--- a/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
+++ b/docs/feature-management-experimentation/10-getting-started/overview/create-an-experiment.md
@@ -64,10 +64,4 @@ You can assign the following metric categories to your experiment any time after
 * Guardrail metrics, which ensure important areas like performance or stability are not negatively impacted.
 * Supporting metrics, which offer additional context, even if they’re not directly related to the experiment’s success criteria.
 
-For more information, see the [Metrics documentation](/docs/feature-management-experimentation/experimentation/metrics/).
-
-## Further Reading
-
-Additional documentation, blog links, and articles:
-
-- [A/B Testing For Feature Flags, What It Is And What It Shouldn’t Be](https://www.harness.io/blog/a-b-testing-for-feature-flags-what-it-is-and-what-it-shouldnt-be)
\ No newline at end of file
+For more information, see the [Metrics documentation](/docs/feature-management-experimentation/experimentation/metrics/).
\ No newline at end of file
diff --git a/docs/feature-management-experimentation/40-feature-management/index.md b/docs/feature-management-experimentation/40-feature-management/index.md
index 0cbe761678e..02f6e219321 100644
--- a/docs/feature-management-experimentation/40-feature-management/index.md
+++ b/docs/feature-management-experimentation/40-feature-management/index.md
@@ -14,22 +14,6 @@ A feature flag allows you to choose between different code paths in your system
 
 When you create a feature flag, you specify metadata, including the flag name, description, owners, and tags. This information helps you and your team manage your feature flags and customize them to your team's workflow.
 
-### Feature flags and SEO
-
-Using feature flags to run experiments or deliver different variations of a page can have some impact on SEO, depending on what content is visible to search engines during crawling. Most of the time, the impact is minimal, especially for short-term tests. That said, long-term experiments that serve vastly different versions of the same page might lead to ranking differences if search engines crawl both versions multiple times.
-
-Web crawlers like Google typically perform their initial crawling with JavaScript disabled. This means `getTreatment()` won’t be called, and the bot will receive the control treatment. On the backend, the bot is usually treated as anonymous traffic and should be assigned a new key, just like you would with a real anonymous user.
-
-To avoid SEO issues, it's best practice to maximize the quality of your page before JavaScript rendering—focusing on static elements like titles, text content, and hrefs. Any SEO in place for that initial crawl will remain unaffected by the feature flag unless you introduce targeting rules that behave differently for bots.
-
-### Cloaking considerations
-
-Cloaking refers to deliberately serving different content to search engines than to real users, which can violate search engine guidelines. Feature flags don’t inherently enable cloaking. However, it is possible to cloak content using user agent targeting, such as detecting a search bot and routing it to a specific treatment. But this would require explicit implementation and is not something feature flags do by default.
-
-### Experimentation and SEO
-
-Both Google and Microsoft have confirmed that A/B testing is safe for SEO when done correctly. If you want to provide a consistent experience for crawlers during experimentation, consider using a consistent key for bots during randomization. Just avoid explicitly targeting user agents for different content, which may raise concerns around cloaking.
-
 ## Ensuring a consistent user experience
 
 ### Random and Deterministic Treatment Assignment
@@ -83,6 +67,22 @@ If you do a 50/50 split by account, large accounts with many users might unevenl
 
 Harness FME ensures a consistent user experience by combining deterministic hashing with seed-based bucketing and configurable limit exposure, enabling reliable and predictable feature rollouts across different users and environments.
 
+### Feature flags and SEO
+
+Using feature flags to run experiments or deliver different variations of a page can have some impact on SEO, depending on what content is visible to search engines during crawling. Most of the time, the impact is minimal, especially for short-term tests. That said, long-term experiments that serve vastly different versions of the same page might lead to ranking differences if search engines crawl both versions multiple times.
+
+Web crawlers like Google typically perform their initial crawling with JavaScript disabled. This means `getTreatment()` won’t be called, and the bot will receive the control treatment. On the backend, the bot is usually treated as anonymous traffic and should be assigned a new key, just like you would with a real anonymous user.
+
+To avoid SEO issues, it's best practice to maximize the quality of your page before JavaScript rendering—focusing on static elements like titles, text content, and hrefs. Any SEO in place for that initial crawl will remain unaffected by the feature flag unless you introduce targeting rules that behave differently for bots.
+
+### Cloaking considerations
+
+Cloaking refers to deliberately serving different content to search engines than to real users, which can violate search engine guidelines. Feature flags don’t inherently enable cloaking. However, it is possible to cloak content using user agent targeting, such as detecting a search bot and routing it to a specific treatment. But this would require explicit implementation and is not something feature flags do by default.
+
+### Experimentation and SEO
+
+Both Google and Microsoft have confirmed that A/B testing is safe for SEO when done correctly. If you want to provide a consistent experience for crawlers during experimentation, consider using a consistent key for bots during randomization. Just avoid explicitly targeting user agents for different content, which may raise concerns around cloaking.
+
 ## Further Reading
 
 Additional documentation, blog links, and articles:
diff --git a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
index 57938a7469a..68bc9d3a01e 100644
--- a/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
+++ b/docs/feature-management-experimentation/40-feature-management/targeting/target-with-custom-attributes.md
@@ -188,10 +188,12 @@ You can also directly click within the IF field, type a new custom attribute ID,
 
 ## Using regex with custom attributes
 
-Split targeting rules support matching values using regular expressions (regex), which provides a powerful way to filter a wide range of users with a single targeting rule.
+Split targeting rules support matching values using regular expressions (regex), which provides a powerful way to filter a wide range of users with a single targeting rule. 
+
+If your versioning follows [semantic versioning](#semver-attributes), you can use SemVer-based targeting instead of regex for cleaner and more maintainable rules. Regex examples are mainly useful for legacy scenarios or non-SemVer attributes.
 
 :::tip
-We recommend testing your regex patterns on external tools like [regex101.com](https://regex101.com/) to ensure they work as expected before applying them in Split.
+Harness recommends testing your regex patterns on external tools like [regex101.com](https://regex101.com/) to ensure they work as expected before applying them in Split.
 :::
 
 Below are some useful regex examples for targeting specific user attributes:
@@ -362,7 +364,7 @@ For example, use an attribute 'os_version' of type SemVer to give users that hav
 
 #### Supported SDKs and customer-deployed components for SemVer matcher
 
-The Semantic Version (SemVer) matcher is only supported in specific FME SDK and customer-deployed componenet versions. If you are using an older SDK version that does not support the SemVer matcher, feature flag evaluations will return the `control` treatment and a special impression will be created.
+The Semantic Version (SemVer) matcher is only supported in specific FME SDK and customer-deployed component versions. If you are using an older SDK version that does not support the SemVer matcher, feature flag evaluations will return the `control` treatment and a special impression will be created.
 
 :::info
 If you are using an older SDK or customer-deployed component version that does not support the SemVer matcher, Harness recommends upgrading to a supported version to ensure your target rules function as expected.
diff --git a/docs/feature-management-experimentation/release-agent/index.md b/docs/feature-management-experimentation/release-agent/index.md
index 01e404238ad..fbe813fdd58 100644
--- a/docs/feature-management-experimentation/release-agent/index.md
+++ b/docs/feature-management-experimentation/release-agent/index.md
@@ -102,4 +102,10 @@ Additional ways that your privacy is protected:
 
 For support inquiries or to report issues with Release Agent, submit a request via support@split.io. 
 
-Provide detailed descriptions and reproduction steps for issues to facilitate expedited troubleshooting. Your feedback is instrumental for the iterative enhancement of Release Agent.
\ No newline at end of file
+Provide detailed descriptions and reproduction steps for issues to facilitate expedited troubleshooting. Your feedback is instrumental for the iterative enhancement of Release Agent.
+
+## Further Reading
+
+Additional documentation, blog links, and articles:
+
+- [AI-Native Software Delivery: Proven Practices to Produce High-Quality Software Faster](https://www.harness.io/resources/ai-native-software-delivery-proven-practices-to-produce-high-quality-software-faster)
\ No newline at end of file