Originally published on AI Tech Connect.
What you need to know Treat prompts and agents like code. Keep a versioned golden test dataset in the repository, run an eval suite against it on every pull request, and fail the build if quality regresses below a threshold. Silent regressions get caught before production, not after. The regression loop is four steps. Take the new prompt or agent version, run it against the standard test dataset, compare scores against the current production baseline, and fail the build if quality, latency or cost regresses. Two tools cover most teams. promptfoo is terminal-first with declarative YAML and first-class GitHub Actions support; DeepEval gives you pytest-native assert-style tests and twenty-plus built-in metrics. Start with one, add custom layers as needs sharpen. Mix assertion types.…
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