No customer fine-tuning of Claude

As a customer you do not train or fine-tune Claude's weights on your own dataset. Instead you steer the model at inference time using everything that goes into the request: the system prompt, few-shot examples, retrieved context (RAG), tool definitions, and output constraints.

This matters because the instinct "the model doesn't know my domain, so I'll fine-tune it" is the wrong lever here. The correct fixes are supplying the domain knowledge in context (via RAG or documents), giving clearer instructions and examples, and wiring tools so the model can fetch or act on live data. These are cheaper, faster to iterate, and auditable.

Behavior you get from good prompting and retrieval is often mistaken for "training" — but nothing about the model's parameters changes. Each request is independent; there is no persisted learning from your prior calls unless you re-supply that context yourself.