Fine-tuning starts from a model that has already learned broad patterns and updates its parameters with a smaller, purposefully chosen dataset. The goal may be better terminology, task performance, style, safety behavior or adaptation to a particular deployment domain.
High-quality in-domain examples can improve performance on conditions the original training mix did not cover well. For a speech system, that might include a company's vocabulary, meeting acoustics or a rare language.
The data mix and training settings matter because narrow adaptation can damage useful general capabilities. Evaluation should compare both the target task and representative general tasks before the tuned model is deployed.
ELI5
Fine-tuning continues training an existing AI model on additional examples so its stored weights better fit a task, field or preferred behavior. It starts from a model that already knows broad patterns instead of learning everything from zero.
For example, a company can fine-tune a model on reviewed examples of its support format. The change remains in the new model version, so teams need evaluations for improvement, regressions and overfitting rather than assuming more training always helps.
Is fine-tuning the same as training from scratch?
No. Fine-tuning begins with an existing trained model and makes further updates using a focused dataset.
What is a risk of fine-tuning?
Overly narrow or low-quality data can make the model less reliable outside the target domain or weaken capabilities it previously had.
