Fine-Tuning Evaluation & Validation
How to evaluate fine-tuned models — metrics by task type, regression testing, and the complete evaluation pipeline.
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After this section you can
- Baseline before training so an improvement claim means something
- Pick the metric a given task type cannot game
- Build a regression suite that catches catastrophic forgetting before a canary does
Fine-Tuning Evaluation & Validation
No eval, no fine-tune. The evaluation pipeline from frozen test set to canary rollout, metrics by task type, catastrophic-forgetting checks, and LLM-as-judge done properly.
“The fine-tuned model feels better” is not a result — it is the placebo effect with a GPU bill. The eval suite is built before training, the ship gates are written down before results exist, and the test set is frozen so nobody — human or training script — can overfit to it. Evaluation is not the last step of fine-tuning; it is the frame the entire project hangs on. Teams that skip it don’t find out the fine-tune failed — their customers do.