I still don't have a really good answer to this question:
If you want to be able to do Q&A against an existing corpus of documentation, can fine-tuning an LLM on that documentation get good results, or is that a waste of time compared to the trick where you search for relevant content and paste that into a prompt along with your question?
I see many people get excited about fine-tuning because they want to solve this problem.
> Although fine-tuning can feel like the more natural option—training on data is how GPT learned all of its other knowledge, after all—we generally do not recommend it as a way to teach the model knowledge. Fine-tuning is better suited to teaching specialized tasks or styles, and is less reliable for factual recall. [...] In contrast, message inputs are like short-term memory. When you insert knowledge into a message, it’s like taking an exam with open notes. With notes in hand, the model is more likely to arrive at correct answers.
If you want to be able to do Q&A against an existing corpus of documentation, can fine-tuning an LLM on that documentation get good results, or is that a waste of time compared to the trick where you search for relevant content and paste that into a prompt along with your question?
I see many people get excited about fine-tuning because they want to solve this problem.
The best answer I've seen so far is in https://github.com/openai/openai-cookbook/blob/main/examples...
> Although fine-tuning can feel like the more natural option—training on data is how GPT learned all of its other knowledge, after all—we generally do not recommend it as a way to teach the model knowledge. Fine-tuning is better suited to teaching specialized tasks or styles, and is less reliable for factual recall. [...] In contrast, message inputs are like short-term memory. When you insert knowledge into a message, it’s like taking an exam with open notes. With notes in hand, the model is more likely to arrive at correct answers.