What is fine-tuning?
Fine-tuning is further training of an existing AI model on a smaller, specific set of examples, so it gets better at a particular task, style or domain. It changes the model's weights, unlike prompting or RAG, which only change what you send it.
How does fine-tuning work?
You prepare many examples of inputs and ideal outputs and train the model on them for a short time. The model keeps its general knowledge but shifts towards your examples.
Why does fine-tuning matter?
Fine-tuning helps with consistent style or narrow tasks at scale. For most needs, better prompts or RAG are cheaper and easier to update.
An example
Example: A company fine-tunes a model on thousands of its past support replies so it answers in the company's voice by default.
Related terms
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