Fine-tuning trains an existing model on many examples so a narrow behavior becomes part of how it acts: classify this record, respond in this format. It changes the model itself. Handing it documents during a task works differently: upload old bids into a project, or build a RAG system that retrieves relevant chunks before answering, and the underlying model stays the same. That makes “trained on your bids” useful client shorthand, though it’s usually retrieval.
Training a new employee through repeated reviewed examples is the closest parallel. You don’t hand them one old proposal and say “copy this.” You give them many, correct the same pattern again and again, and the habit gets built in.
Why you care
Fine-tuning is heavy. You need examples in the right shape, expected outputs, and a way to test that the tuned model actually improved; if your process changes every two weeks, it’s the wrong tool. For most workflows, RAG or better prompting wins first: retrieval for fresh facts, examples and a good schema for consistent structure. The question worth keeping: are you teaching the model itself, or just giving it better material to work from?