llmobs: misc instrumentation doc improvements

config/_default/menus/main.en.yaml

@@ -4648,7 +4648,7 @@ menu:
       parent: llm_obs_instrumentation
       identifier: llm_obs_instrumentation_custom
       weight: 202
-    - name: API
+    - name: HTTP API
       url: llm_observability/instrumentation/api
       parent: llm_obs_instrumentation
       identifier: llm_obs_instrumentation_api

content/en/llm_observability/instrumentation/_index.md

@@ -13,6 +13,12 @@ You can visualize the interactions and performance data of your LLM applications
 
 {{< img src="llm_observability/traces.png" alt="An LLM Observability trace displaying each span of a request" style="width:100%;" >}}
 
+
+## Further Reading
+
+{{< partial name="whats-next/whats-next.html" >}}
+
+
 [1]: /llm_observability/auto_instrumentation
 [2]: /llm_observability/setup/api
-[3]: https://app.datadoghq.com/llm/traces
+[3]: https://app.datadoghq.com/llm/traces

content/en/llm_observability/instrumentation/api.md

@@ -1,5 +1,5 @@
 ---
-title: API Reference
+title: HTTP API Reference
 aliases:
     - /tracing/llm_observability/api
     - /llm_observability/api
@@ -8,7 +8,7 @@ aliases:
 
 ## Overview
 
-The LLM Observability API provides an interface for developers to send LLM-related traces and spans to Datadog. If your application is written in Python or Node.js, you can use the [LLM Observability SDKs][1].
+The LLM Observability HTTP API provides an interface for developers to send LLM-related traces and spans to Datadog. If your application is written in Python or Node.js, you can use the [LLM Observability SDKs][1].
 
 The API accepts spans with timestamps no more than 24 hours old, allowing limited backfill of delayed data.
 

content/en/llm_observability/instrumentation/auto_instrumentation.md

@@ -11,7 +11,6 @@ further_reading:
       text: 'Learn about the LLM Observability SDK for Python'
 ---
 
-<div class="alert alert-info">Datadog offers a variety of artificial intelligence (AI) and machine learning (ML) capabilities. The <a href="/integrations/#cat-aiml">AI/ML integrations on the Integrations page and the Datadog Marketplace</a> are platform-wide Datadog functionalities. <br><br> For example, APM offers a native integration with OpenAI for monitoring your OpenAI usage, while Infrastructure Monitoring offers an integration with NVIDIA DCGM Exporter for monitoring compute-intensive AI workloads. These integrations are different from the LLM Observability offering.</div>
 
 {{< tabs >}}
 {{% tab "Python" %}}

content/en/llm_observability/instrumentation/custom_instrumentation.md

@@ -25,8 +25,6 @@ This page explains how to use the Datadog LLM Observability SDK's custom instrum
 
 ## Instrument an LLM application
 
-<div class="alert alert-info">These instructions use the <a href="/llm_observability/setup/sdk">LLM Observability SDK for Python</a>. If your application is running in a serverless environment, follow the <a href="/llm_observability/setup/sdk/#aws-lambda-setup">serverless setup instructions</a>. </br></br> If your application is not written in Python, you can complete the steps below with API requests instead of SDK function calls.</div>
-
 To instrument an LLM application:
 
 1. [Install the LLM Observability SDK for Python][5].

content/en/llm_observability/instrumentation/sdk.md

@@ -2283,6 +2283,3 @@ tracer.use('http', false) // disable the http integration
 [11]: /tracing/trace_collection/compatibility/python/#integrations
 [12]: /tracing/trace_collection/compatibility/python/#library-compatibility
 [13]: /llm_observability/instrumentation/auto_instrumentation/
-[14]: /serverless/aws_lambda/installation/python/?tab=custom#installation
-[15]: /llm_observability/quickstart?tab=python#trace-an-llm-application-in-aws-lambda
-[16]: https://app.datadoghq.com/llm/settings/evaluations

content/en/llm_observability/quickstart.md

@@ -3,15 +3,12 @@ title: Quickstart
 aliases:
     - /tracing/llm_observability/quickstart
 further_reading:
-    - link: '/llm_observability'
-      tag: 'Documentation'
-      text: 'Learn about LLM Observability'
     - link: '/llm_observability/evaluations'
       tag: 'Evaluations'
       text: 'Configure Evaluations on your application'
     - link: '/llm_observability/instrumentation/custom_instrumentation'
       tag: 'Custom Instrumentation'
-      text: 'Custom instrumentation'
+      text: 'Instrument your application with custom spans'
 ---
 
 This page demonstrates using Datadog's LLM Observability SDK to instrument a Python or Node.js LLM application.