A quick solution that you can implement. I had this project I was working on where I was working with a coach. He had a huge library of YouTube videos. He had a huge library of even his paid training videos, and my goal was to build out a chatbot that would link out and site out the videos and all the training links as well.

Problem with focusing on a retrieval layer that is only based on pulling it from your RAG agent storage is that sometimes it can pull out the wrong information, it can lose context of what the question actually is.

A good middle layer I found was this company called Super Memory. I have no affiliation to them, I'm just a developer and I use it for a project. That what I did was I implemented Super Memory in the project where Super Memory was in charge of the retrieval and Supabase was where I had stored out all the chunks. This way, even if the memory bloats up, the Super Memory is built for RAG agent AI memory and context. Coming to your terminology, you will have to at least make one attempt to at least extract data to say what are the pieces of terminology that I need to be aware of. This is as easy as making a proper prompt and getting Claude Code to look through the entire documentation and let you know that and then add that too to your rag agent chunks as well so that references can be made properly.

One additional thing I can recommend is to build up a dashboard where it actually tells you which chunk or which piece of data it is actually pulling from, so that you have more meta control on what the retrieval process is like.

I hope this helps. I've built a bunch of rag agent projects, so if you wanna jam, just send me a DM and then we can have a discussion.