Does anyone know of any good write-ups on how to carry out this sort of task, for people who are reasonably technical (i.e., know how to code) but aren’t deep in the AI world? I feel like “customize a model based on a corpus of documents” (whether that’s “fine-tuning” or “RAG”) is a thing that everyone wants to know how to do but nobody actually explains in straightforward terms. (I pay for Gemini solely for access to NotebookLM for these purposes, but it would be nice to just be able to roll my own locally.)

I've heard of people using anythingllm for this purpose.

Basic rag is almost stupid in how easy it is, though. You grep for keywords, take the surrounding paragraph, then stuff it all into your llm prompt.

The next upgrade is to automate keyword extraction by putting your documents into a vector store and search by vector similarity.