Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to specialize its behavior for particular tasks or domains. Unlike prompt engineering, which works within a model's existing capabilities, fine-tuning permanently modifies the model's weights to improve performance on targeted use cases.
A company fine-tunes GPT-4 on thousands of their customer support conversations so the model learns the company's tone, product terminology, and common resolution patterns without needing lengthy prompts each time.
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