You are building a language model feature that must answer questions using domain knowledge, but the team is deciding whether to update the model weights or connect it to an external knowledge store. The same system may need to reflect changing facts, policy text, and terminology without retraining every time.
What is the precise difference between fine-tuning and retrieval-augmented generation (RAG)?
