This may not be directly related to llm but I am curious about two things -

1. How do llm/rag generate an answer given a list of documents and a question? I can do bm25 to get a list of documents, but post that what is logic/algorithm which generates answers given those list?

2. For small models like this, how much data you need to fine tune for a specific use case? For eg, if I need this model to be knowledgable about html/css, then I have access to lot of documentation online that I can feed it. But if it is very specific topic, like types of banana, then it may be only a couple of wikipedia pages. So is fine tuning directly dependant on the quantity of data alone?