Continuous Eval Pipelines
Evaluation is not a one-time event — it is a continuous pipeline that runs on every prompt change, gates deployments, detects regressions, and monitors production quality. This article builds the complete CI/CD evaluation pipeline: from GitHub Actions integration to quality gates, regression detection, and production monitoring with alerting.
Quick Reference
- →Run evals on every prompt/code change in CI — treat prompt changes like code changes
- →Quality gates: define minimum scores that must pass before deployment
- →Regression detection: compare new scores to a baseline and flag significant drops
- →Production monitoring: sample live traffic, score with automated judges, track trends
- →Alert thresholds: page humans when quality drops below critical levels
- →Store eval results in a time-series database for trend analysis
CI/CD for AI: Running Evals on Every Change
Prompt changes are code changes. A one-word prompt modification can shift quality by 20%. Yet most teams treat prompts as configuration that bypasses CI/CD entirely. A mature AI team runs automated evaluations on every prompt change, just like they run tests on every code change. The eval pipeline should be fast enough for developer iteration (< 5 minutes for a core eval set) and comprehensive enough to catch regressions.
Store prompts in files tracked by git, not in databases or environment variables. This gives you diff visibility, branch-based testing, and the ability to run evals against any prompt version. A prompt change should produce a pull request with eval results in the PR comments.