What is fine-tuning in AI?
Fine-tuning is the process of further training an existing model on a specific dataset so it adapts to a particular style, format, or domain. It changes the model's weights, which makes it well suited to teaching behaviour and poorly suited to teaching facts that change.
The most common mistake is reaching for fine-tuning to give a model knowledge. Facts embedded in weights cannot be updated without retraining, cannot be permissioned, and cannot be cited — three properties that retrieval provides for free.
Where fine-tuning earns its cost is consistency of behaviour: enforcing a house tone, reliably producing a strict output format, or handling domain reasoning that no amount of prompting gets right. In practice most production systems use both — retrieval for what the model needs to know, fine-tuning for how it should respond.
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