Earlier quoted context omitted.
What is the general thought process on when it makes sense to use RAG vs fine tuning? How does segmenting fine tuning models make sense? Do I need a terraform LLM, a SQL LLM, and a python LLM, or can I just use a “code” LLM?
Fine tuning for training the model to perform a new task, RAG for adding knowledge. In your example, you would fine tune the model to train it to code in a language it hasn't seen before, RAG will not really help with that.
I've read that SFT is good for "leveraging existing knowledge" gained during initial pretraining, and helpful in changing the way that the model responds, but not useful for teaching it new knowledge. In your experience is that true?
For example, changing the way in which it responds could be:
- debate me
- brainstorm
- be sarcastic
Alternatively, gaining new knowledge would be training it on a dataset of e.g. sports trivia to make it highly effective at answering those types of questions.
P.S. nice username... Irving Fisher would approve.