Model Training & Adaptation
Fine-tuning
Additional training that adapts an existing model to a particular task, style, or domain.
Example
A general language model is trained further on approved legal examples for contract review.
Why it matters
It helps a tool follow specialized patterns that ordinary instructions do not capture reliably.
You'll hear it
“The adapted version handles our writing style more consistently.”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- When is fine-tuning unnecessary?
- It may be unnecessary when better instructions, examples, search or another existing tool already solves the problem.
- Does fine-tuning add current facts to a model?
- It can change learned information, but looking up current sources is often easier to keep accurate as facts change.
- What should be measured after fine-tuning?
- Check the intended improvement, new mistakes on other tasks, response time and cost against the earlier version.