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Fix typos and clean style #2042

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16 changes: 8 additions & 8 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 @@ -62,7 +62,7 @@ choose_evaluator_llm.md
**Evaluation**


Here we will use [AspectCritic](../concepts/metrics/available_metrics/aspect_critic.md), which an LLM based metric that outputs pass/fail given the evaluation criteria.
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
Expand All @@ -88,7 +88,7 @@ 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 Expand Up @@ -148,7 +148,7 @@ Output
{'summary_accuracy': 0.84}
```

This score shows that out of all the samples in our test data, only 84% of summaries passes the given evaluation criteria. Now, **It
This score shows that out of all the samples in our test data, only 84% of summaries passes the given evaluation criteria. Now, **it
s important to see why is this the case**.

Export the sample level scores to pandas dataframe
Expand Down Expand Up @@ -187,4 +187,4 @@ If you want help with improving and scaling up your AI application using evals.

## Up Next

- [Evaluate a simple RAG application](rag_eval.md)
- [Evaluate a simple RAG application](rag_eval.md)