Evaluation pipelines that survive a model swap
The eval harness we ship with every LLM engagement, and how to wire it into CI.
Models change every few months. If your only test for an AI feature is 'it looked good in the demo,' every model upgrade is a gamble. The fix is an evaluation harness you own, wired into CI, that runs before anything ships.
The harness has three layers. A golden set of real inputs with known-good outputs catches regressions on the cases you care about most. A rubric-based grader scores open-ended responses for groundedness, tone, and task completion. And a set of adversarial cases probes the failure modes specific to your domain — the prompts that have burned you before.
Wire it into continuous integration so a model or prompt change runs the full suite automatically and blocks the merge if scores drop. That single gate turns a model swap from a leap of faith into a routine pull request.
The harness is also the artifact that makes buy-versus-build decisions concrete: when you can measure quality objectively, you can compare a vendor's model to your own on your data, and the argument stops being about vibes.
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