At a fundamental level, all you need to know is:

- Read in the user's input

- Use that to retrieve data that could be useful to an LLM (typically by doing a pretty basic vector search)

- Stuff that data into the prompt (literally insert it at the beginning of the prompt)

- Add a few lines to the prompt that state "hey, there's some data above. Use it if you can."

You can start by reading up about how embeddings work, then check out specific rag techniques that people discovered. Not much else is needed really.

Here's a blog post that I just pushed that doesn't use them at all - https://blog.dagworks.io/p/building-a-conversational-graphdb (we have more on our blog - search for RAG).

[disclaimer I created Hamilton & Burr - both whitebox frameworks] See https://www.reddit.com/r/LocalLLaMA/comments/1d4p1t6/comment... for comment about Burr.

My strategy has been to implement in / follow along with llamaindex, dig into the details, and then implement that in a less abstracted, easily understandable codebase / workflow.

Was driven to do so because it was not as easy as I'd like to override a prompt. You can see how they construct various prompts for the agents, it's pretty basic text/template kind of stuff

This is fun and interesting:

https://developers.cloudflare.com/workers-ai/tutorials/build...

Data centric on YouTube has some great videos . https://youtube.com/@data-centric?si=EOdFjXQ4uv02J774

check this: https://www.lycee.ai/blog/rag-fastapi-postgresql-pgvector

openai cookbook! Instructor is a decent library that can help with the annoying parts without abstracting the whole api call - see it’s docs for RAG examples.