Fine-Tuning
Further training an existing model on your own examples so it adopts a specific style, format, or specialty.
It bakes behavior into the model itself, useful when prompting alone cannot get consistent results.
Frequently asked questions
What is fine-tuning in plain language?
Fine-tuning is extra training of an existing model on your own examples so it reliably adopts a specific style, format, or specialty. Instead of explaining what you want every time, you bake the behavior into the model itself.
Do I need to fine-tune a model, or can I just write good prompts?
Most needs are met by good prompting and examples, which are cheaper and faster to change. Fine-tuning is worth it when prompting alone cannot get consistent results and you need the same specialized behavior at scale.
What is the difference between fine-tuning and few-shot examples?
Few-shot means showing examples inside the prompt each time, which is quick and easy to adjust. Fine-tuning trains those patterns permanently into the model, so it acts that way without needing the examples repeated every time.
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