AI Glossary Term
What does “Fine-Tuning” mean?
Continuing training on domain-specific data to adapt a pre-trained model to a specific task.
How Fine-Tuning works
Example
A law firm starts from a general language model and continues training it on its own contract summaries. The tuned model then follows the firm's formatting and terminology more closely than the generic model.
Why it matters
Fine-tuning reuses expensive pre-training and adapts it with far less data and compute. It can also change a model's behavior in unwanted ways, so results need testing.
Common questions
When should I fine-tune instead of prompting?
When prompts and examples are not enough, or when you need consistent style or format at scale. Try prompting and retrieval first.
Does fine-tuning add new knowledge?
It can shift style and behavior well; for adding facts, retrieval is often more reliable.
Can fine-tuning break safety features?
It can weaken them, so tuned models need to be tested again.