Fine-tuning — Model Fine-tuning
Definition: Continuing the training of a pre-trained model on your own data to specialize its behavior. Powerful, but often the wrong first move — prompt engineering and retrieval usually beat fine-tuning on cost and time-to-value.
Example
We fine-tuned because the prompt-only version plateaued at 78% — past that point, only weights moved the number.
When you'll hear it
Fine-tuning shows up most often in AI strategy reviews, model evaluation discussions, and product roadmap meetings. When someone uses it, they're usually referring to model fine-tuning — and they expect the room to already know what that means.
FAQs
What does Fine-tuning stand for?
Fine-tuning stands for Model Fine-tuning.
What does Fine-tuning mean in AI and machine-learning?
Continuing the training of a pre-trained model on your own data to specialize its behavior. Powerful, but often the wrong first move — prompt engineering and retrieval usually beat fine-tuning on cost and time-to-value.
Where will I hear Fine-tuning used at work?
Fine-tuning comes up most often in AI strategy reviews, model evaluation discussions, and product roadmap meetings. It's used as shorthand for model fine-tuning, so people assume you already know the term.