I think your question is asking about the fundamentals of how an LLM works, which I'm not really qualified to answer. But I do have a general understanding of i

I think your question is asking about the fundamentals of how an LLM works, which I'm not really qualified to answer. But I do have a general understanding of it all. Fine-tuning is like having the model take a class on a certain subject. By the end of the class, it's going to have a general understanding on how to do that thing, but it's probably going to struggle when trying to quote the textbooks verbatim. A good use-case for fine-tuning is teaching it a response style or format. If you fine-tune a model to only respond in JSON, then you no longer need to include formatting instructions in your prompt to get a JSON output.