Asked about The RAG pipeline
When would you fine-tune instead of using RAG?
An answer that works
Fine-tuning teaches form: a consistent output format, a domain vocabulary, a house style. RAG supplies facts. If the problem is that the model does not know something, RAG is the answer, because fine-tuning does not reliably install new facts and cannot be updated without retraining.
Then they push further
A team wants to fine-tune a model on their docs so it knows the product. Good idea?
Usually not. Fine-tuning is unreliable at adding facts, cannot be updated when a document changes without training again, and cannot cite a source. Retrieval keeps knowledge current and checkable. Fine-tuning fits problems of behaviour, format or style, not knowledge.
Can you use both?
Yes. Retrieve for the facts and fine-tune for how to use them: a house citation style, a strict output format, or making a small model good enough at the task to replace a larger one.
What gets marked up
Strong answers separate knowledge from behaviour and mention keeping content current and citing sources. Weak answers treat fine-tuning as the more serious or advanced option.
Reading an answer is not being able to give one. The section that teaches this is The RAG pipeline, 4 hours of Retrieval-augmented generation.
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