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Fix typos and clean style #2042

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Aug 28, 2025
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Fix typos and clean style #2042

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Fix typos and clean style
kwinkunks May 16, 2025
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Merge branch 'main' into kwinkunks-fix-typo-in-docs
anistark Aug 28, 2025
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12 changes: 5 additions & 7 deletions docs/getstarted/evals.md
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Expand Up @@ -11,7 +11,7 @@ In this guide, you will evaluate a **text summarization pipeline**. The goal is

### Evaluating using a Non-LLM Metric

Here is a simple example that uses `BleuScore` score to score summary
Here is a simple example that uses `BleuScore` to score a summary:

```python
from ragas import SingleTurnSample
Expand Down Expand Up @@ -40,9 +40,9 @@ Here we used:

As you may observe, this approach has two key limitations:

- **Time-Consuming Preparation:** Evaluating the application requires preparing the expected output (`reference`) for each input, which can be both time-consuming and challenging.
- **Time-consuming preparation:** Evaluating the application requires preparing the expected output (`reference`) for each input, which can be both time-consuming and challenging.

- **Inaccurate Scoring:** Even though the `response` and `reference` are similar, the output score was low. This is a known limitation of non-LLM metrics like `BleuScore`.
- **Inaccurate scoring:** Even though the `response` and `reference` are similar, the output score was low. This is a known limitation of non-LLM metrics like `BleuScore`.


!!! info
Expand All @@ -51,7 +51,7 @@ As you may observe, this approach has two key limitations:
To address these issues, let's try an LLM-based metric.


### Evaluating using a LLM based Metric
### Evaluating using a LLM-based Metric


**Choose your LLM**
Expand All @@ -61,10 +61,8 @@ choose_evaluator_llm.md

**Evaluation**


Here we will use [AspectCritic](../concepts/metrics/available_metrics/aspect_critic.md), which is an LLM based metric that outputs pass/fail given the evaluation criteria.


```python
from ragas import SingleTurnSample
from ragas.metrics import AspectCritic
Expand All @@ -88,7 +86,7 @@ Output
Success! Here 1 means pass and 0 means fail

!!! info
There are many other types of metrics that are available in ragas (with and without `reference`), and you may also create your own metrics if none of those fits your case. To explore this more checkout [more on metrics](../concepts/metrics/index.md).
There are many other types of metrics that are available in `ragas` (with and without `reference`), and you may also create your own metrics if none of those fits your case. To explore this more checkout [more on metrics](../concepts/metrics/index.md).

### Evaluating on a Dataset

Expand Down
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