Evaluation Gate
An evaluation gate is an automated quality checkpoint that scores an AI workflow against curated test cases before a change ships. Prompts, retrieval settings, or pack updates must pass thresholds for accuracy, grounding, and safety; failing changes are blocked from release. Gates turn AI quality from a hope into an enforced, repeatable engineering practice.
Synonyms: eval gate, quality gate, release gate, evaluation harness
An evaluation gate applies the discipline of a CI test suite to AI behavior. Because model outputs are probabilistic, a change that looks harmless — a reworded prompt, a new model version, a retrieval tweak — can silently degrade answer quality. A gate makes that regression visible before users see it: the candidate configuration runs against a dataset of representative cases, scorers grade the outputs for accuracy, grounding, and safety, and the release is blocked if any threshold fails. Over time the dataset grows with real edge cases from production, so the gate becomes a living contract for what “good” means in that workflow.