Fine-tuning

You already have a model that can talk. Fine-tuning is when you keep training it on your examples until a habit sticks.

Not a new encyclopedia. A new reflex.

You collect pairs: this kind of ticket, this kind of reply. This kind of user message, this exact JSON shape. You run a training job. Afterward the model reaches for your format, your tone, your refusals, without you restating the house rules every turn. The system prompt is still cheaper. Fine-tuning is what you reach for when the prompt is not enough and the same shape repeats a thousand times.

Hold it next to RAG, because "make it know our stuff" is usually that fork:

• Facts that change — prices, inventory, last week's numbers, the FAQ you edited at 2 a.m. — do not belong in weights. Retrieval owns those. Running another training job every time a price moves is a bad weekend.
• Shape and style — always sound like support, always emit this schema, always refuse that class of ask — can live in a fine-tune. You are teaching habits, not today's spreadsheet.

The costs are real. You need a pile of good examples, a training run, and an eval set so you can tell "it sounds like us" from "it broke the easy cases." A bad fact baked into the run comes back later as a confident hallucination you cannot fix by editing a page. You train again.

At a hackathon, skip it. A tight system prompt plus RAG plus a few tool calls will beat a rushed fine-tune. Ship the habit in instructions first. Bake it into weights when the product is the habit.

What this unlocks

You stop treating "custom model" as the default. You pick the cheap lever: prompt for rules, retrieval for facts, fine-tune only when the way it writes is the product.