Fine-tuning
Training a pre-built model further on your own data, to make it better at a specific task or voice.
Why it matters
Fine-tuning takes a general-purpose AI model and specializes it for your specific use case. A fine-tuned model for medical records will outperform a generic model on that task while using less compute per request.
Knowing when to fine-tune versus when to use prompt engineering helps you avoid unnecessary costs - fine-tuning is powerful but not always the right choice.
How it works
4 stepsRelated terms
From the glossaryFrequently asked questions
Is fine-tuning the same as training from scratch?+
No. Training from scratch builds all model knowledge from random weights using massive datasets and compute. Fine-tuning starts from an already capable model and nudges it toward a specific style or domain.
How much data do I need to fine-tune?+
Far less than pre-training. Hundreds to a few thousand high-quality examples are often enough to shift style or add domain vocabulary. More data helps for complex tasks.
When should I use fine-tuning instead of prompting?+
When the desired behaviour is consistent and hard to describe in a prompt alone, when you need a specific tone or format every time, or when latency and cost make long system prompts impractical.
See the tools that use it.
The fastest way to understand Fine-tuning is to see it inside real products. Browse hand-reviewed tools that put it to work, each one checked by a person before it was listed.
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