LocalLLaMA

The way to do this is to generate a bunch of hypothetical questions from the FAQ, index these in the vDB

Then for the user prompt do a two stage inference with very small CTX size which only determines if the user is asking a question related to items specifically mentioned on the FAQ. Then you can retrieve the relevant FAQ section or source document accordingly only if the score is within a threshold

RAG is for KNOWLEDGE - not style. THough you may use RAG on a guidance input to select instructions and send them into the system prompt. The whole thing is a LOT more complicated than most people make it - a LOT, sadly.

For example, you must GENERATE the questions - you can not use user input for 2 reasons. First, let's be clear - questions should be precise, so you can not feed in chat history because it may pollute the vector with irrelevant other stuff. Here are the 2 problems:
* Questions may be reflexive. "Where is Microsoft located" is a good question, but then following with "And how many people does the city have" - UNLESS you take information from the first question, can not be answered.
* Questions may not be singular questions. People ahve a tendency to sometimes ask multiple things in one input.

So, on that side you must use an AI to EXTRACT the ACTIVE questions from the chat history, so an AI can extract complete questions, and separate them, then use them separately for RAG.

On the other side, preprocessing into Q&A pairs, (with information density increase AND not vectorizing the answer) and dream like sequences to combine similar vectors are other approaches to make the answer more relevant.

But you must STRICTLY separate what you look for - Rewriting an email, you CAN use RAG to instruct the AI, but it is a little more complex.

This is one of the reasons I look forward to hopefully soon be able to use 32k and 64k context - I fill 16k up pretty much always.