Updated docs

docs/docs/metrics-ragas.mdx

@@ -13,8 +13,18 @@ The RAGAS metric is the average of four distinct metrics:
 
 It provides a score to holistically evaluate of your RAG pipeline's generator and retriever.
 
-:::note
-The `RAGASMetric`, although similar to `deepeval`'s default RAG metrics, is not capable of generating a reason.
+:::warning WHAT'S THE DIFFERENCE?
+The `RAGASMetric` uses the `ragas` library under the hood and are available on `deepeval` with the intention to allow users of `deepeval` can have access to `ragas` in `deepeval`'s ecosystem as well.
+
+They are implemented in almost an identical way to `deepeval`'s default RAG metrics, and however there are a few differences, including but not limited to:
+
+- `deepeval`'s RAG metrics generates a reason that corresponds to the score equation. Although both `ragas` and `deepeval` has equations attached to their default metrics, `deepeval` incorperates an LLM judges' reasoning along the way.
+- `deepeval`'s RAG metrics are debuggable - meaning you can inspect the LLM judges' judgements along the way to see why the score is a certain way.
+- `deepeval`'s RAG metrics are JSON confineable. You'll often meet `NaN` scores in `ragas` because of invalid JSONs generated - but `deepeval` offers a way for you to use literally any custom LLM for evaluation and [JSON confine them in a few lines of code.](guides-using-custom-llms)
+- `deepeval`'s RAG metrics integrates **fully** with `deepeval`'s ecosystem. This means you'll get access to metrics caching, native support for `pytest` integrations, first-class error handling, available on Confident AI, and so much more.
+
+Due to these reasons, we highly recommend that you use `deepeval`'s RAG metrics instead. They're proven to work, and if not better according to [examples shown in some studies.](https://arxiv.org/pdf/2409.06595)
+
 :::
 
 ## Required Arguments
@@ -28,6 +38,14 @@ To use the `RagasMetric`, you'll have to provide the following arguments when cr
 
 ## Example
 
+First, install `ragas`:
+
+```console
+pip install ragas
+```
+
+Then, use it within `deepeval`:
+
 ```python
 from deepeval import evaluate
 from deepeval.metrics.ragas import RagasMetric