What Is Fine-Tuning?
Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to adapt its behavior — teaching it a style, format, or narrow skill.
Fine-tuning is the process of further training a pre-trained model on a focused dataset to adapt its behavior — teaching it a consistent style, output format, or narrow skill.
Why it matters
Fine-tuning changes how a model behaves, not what current facts it knows. It’s the right tool when you need consistent tone, a specific output structure, or strong performance on a narrow, stable task — and the wrong tool for keeping a model up to date on changing knowledge.
Fine-tuning vs. RAG
To give a model access to current or private knowledge, use retrieval-augmented generation. To change its behavior, fine-tune. Many production systems combine both. See our full RAG vs. fine-tuning comparison.
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