AI Model Regression Testing
AI model regression testing verifies that a new version of a model — retrained, fine-tuned, or swapped for a different provider — doesn't perform worse than the previous version on the tasks that matter, using a consistent golden dataset and evaluation criteria across versions to make the comparison meaningful.
The same core idea as traditional regression testing applies here — confirm a change didn't break what already worked — but with the added complexity that "correct" for an AI model is often a graded quality score, not a simple pass/fail, and small overall metric improvements can hide meaningful regressions on specific important cases.
A disciplined practice runs the new model against the same fixed golden dataset the old model was evaluated on, comparing scores side by side, and specifically flags any individual case where performance dropped — even if the aggregate average improved — since averages can mask a serious regression on a narrower but important subset of cases.
