What is fine-tuning?
Training an existing model further on your own examples to change how it behaves.
Knowledge hub
What is fine-tuning?
Fine-tuning continues training an already-trained model on your own examples so it adopts a particular tone, output format or classification behaviour. It changes how the model responds, not what facts it knows.
When it is worth it
- You need consistent tone or house style across thousands of generations.
- You need output in a rigid format that prompting alone does not reliably produce.
- You are classifying into categories specific to your business.
- You want a smaller, cheaper model to match a larger one on one narrow task.
When it is the wrong tool
When you want the model to know current facts. A fine-tune captures a snapshot; the day your pricing changes, the model is wrong and retraining is the only fix. Retrieval handles facts far better and updates instantly.
It also needs real training data — typically hundreds to thousands of high-quality examples. Assembling those is usually the bulk of the cost, not the training run.
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A short note is enough. You'll hear back from the team, not a bot — usually within one working day.
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