Remove irrelevant text/documents tailored to your specific task. Reformat indexed data to match end users expected formats.

Key points about your question from this article https://www.linkedin.com/pulse/maximizing-rag-strategies-enhanced-performance-relevance-reddy-0oukc/

10 Ways to Improve the Performance of Retrieval Augmented Generation 1. Clean your data. RAG connects the capabilities of an LLM to your data. If your data is confusing, in substance or layout, then your system will suffer. If you’re using data with conflicting or redundant information, your retrieval will struggle to find the right context. And when it does, the generation step performed by the LLM may be suboptimal. Say you’re building a chatbot for your startup’s help docs and you find it is not working well. The first thing you should take a look at is the data you are feeding into the system. Are topics broken out logically? Are topics covered in one place or many separate places? If you, as a human, can’t easily tell which document you would need to look at to answer common queries, your retrieval system won’t be able to either. This process can be as simple as manually combining documents on the same topic, but you can take it further. One of the more creative approaches I’ve seen is to use the LLM to create summaries of all the documents provided as context. The retrieval step can then first run a search over these summaries, and dive into the details only when necessary. Some framework even have this as a built in abstraction.

From https://towardsdatascience.com/10-ways-to-improve-the-performance-of-retrieval-augmented-generation-systems-5fa2cee7cd5c

There are a lot of great articles about this subject. There are also YouTube videoes about this as @glamorous-guitar-39983 said.

And I think just watching videos about RAG, and learning how it works is enough.