Development · Term
Fine-tuning
Fine-tuning is the process of further training an existing model on your own examples so it adopts a specific format, tone, or task behaviour more reliably than prompting alone achieves.
Fine-tuning changes how a model behaves, not what it knows. It is effective for enforcing a consistent output structure or house style, and ineffective as a way to teach a model facts — for factual grounding, retrieval is the correct tool.
It is usually the wrong first move. Better prompting, clearer output schemas, and retrieval solve most problems teams reach for fine-tuning to fix, without the cost of building a training set or the burden of maintaining it as models change.
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