[bonniemuffin]

How did you choose between training a model from scratch vs using retrieval augmented generation with an existing off-the-shelf model? From what I've observed, RAG + off-the-shelf model seems to be the more common approach for use cases like "create LLM that answers questions about my company's internal documentation", particularly because the iteration/improvement cycle is much shorter-- it's much easier to iterate on RAG/prompts vs. training a whole new model to improve it. (If the answer is "I just wanted to try training a whole new llm", I won't fault you for that! :) )