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
- Score the right baselines before training so an improvement claim means something
- Pick the metric per task type and judge a fine-tune pairwise against its baseline
- Build ship gates, a forgetting regression suite and a canary for a fine-tuned model
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Fine-Tuning Evaluation & Validation
A fine-tune is only better than something you measured first. Baselines, a metric per task type, a judge used properly, a regression suite for forgetting, and a canary before full traffic.
Key idea
Build the eval before you train. Score the best prompted baseline on a frozen test set, write the ship gates down, and let the candidate pass them on its task and on everything it might have forgotten. Then canary it, because production traffic always holds surprises.
The evaluation frame: baselines first, gates before shipping, canary before everyone