The key distinction: fine-tuning changes the model itself, unlike handing it documents to read during a task. You start with a model that already knows a lot, then train it on examples so it gets better at a narrow behavior: classify this record, respond in this format, follow this labeling pattern.
Think about training a new employee through repeated reviewed examples. 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. That’s the shape of fine-tuning.
How it shows up
Giving an AI your documents usually isn’t fine-tuning. Upload old bids or transcripts into a project and the model uses them as structured or unstructured context; build a RAG system and it retrieves relevant chunks before answering. In both cases the underlying model isn’t changing. That makes “trained on your bids” useful client shorthand, though it’s often retrieval or a scoring workflow, not fine-tuning.
Why you care
Fine-tuning is heavier: you need examples in the right shape, expected outputs, and a way to test that the tuned model is actually better. If your process changes every two weeks, it’s the wrong tool. For most workflows, RAG or better prompting is the better first move: retrieval for fresh facts, examples and a good schema for consistent structure. Embeddings live in the retrieval path, not the fine-tuning path. The question worth keeping: are you teaching the model itself, or just giving it better material to work from?