regression-testing

You can detect AI regressions by running curated input and expected behavior test suites against new model versions, comparing pass rates to baselines with configurable thresholds, and generating structured triage reports to identify failures.

You can integrate regression testing into CI/CD by configuring gating hooks that automatically run eval suites on pull requests, scheduled model upgrades, and periodic checks to block merges when pass rates fall below established thresholds.

Automated AI eval suites prevent broken user-facing behavior by validating prompt modifications, context shifts, and toolchain changes against baseline pass rates before deployment, producing triage reports with rollback recommendations for detected failures.

The best way to build a regression test set for AI features is to curate input and expected behavior pairs drawn from past failures, edge cases, and representative happy paths, enabling baseline pass-rate comparisons during model updates.

You need existing AI eval suites or the ability to create curated input and expected behavior test sets to establish baseline pass rates, enabling meaningful regression detection when running checks on pull requests or scheduled model upgrades.

AI features break after prompt modifications or context changes because altered inputs shift model behavior, which regression testing catches by running eval suites against baselines and producing triage reports with root-cause hypotheses and fix recommendations.