Better Context for your RAG with Contextual Retrieval

Better chunks = better RAG?

No matter how advanced your model (LLM) is, if the context chunks don’t provide the right information, the model won’t generate accurate answers. In this tutorial, we’ll explore a technique called contextual retrieval to improve the quality of context chunks in your RAG systems.

To give you a better understanding, let’s start with a simple example. Imagine you have a document with multiple chunks, and you want to ask a question based on one of them. Let’s have a look at a sample chunk:

For more information, please refer to 
[the documentation of `vllm`](https://docs.vllm.ai/en/stable/).
 
Now, you can have fun with Qwen2.5 models.

This is a good example of a chunk that could benefit from additional context. In itself, it’s not very informative. Let’s look at the one with added context:

For more information, please refer to 
[the documentation of `vllm`](https://docs.vllm.ai/en/stable/).
 
Now, you can have fun with Qwen2.5 models.
The chunk is situated at the end of the document, following the section on 
deploying Qwen2.5 models with vLLM, and serves as a concluding remark 
encouraging users to explore the capabilities of Qwen2.5 models.