How to Build a CI Gate That Catches LLM Output Regressions Before Users Do

A software developer specializing in LLM evaluation has outlined a lightweight, dependency-free method to detect regressions in large language model outputs during the CI/CD pipeline. The approach stores evaluation data as plain JSON files containing questions, expected answers, and match rules, then scores model responses using a single Python script that exits with a non-zero code on failure. Smart matching strategies — including substring checks, numeric tolerance comparisons, and text normalization — reduce false negatives that would otherwise mask real issues. Beyond simple accuracy scores, the method diffs results between two runs to pinpoint which specific test cases regressed, ensuring that an overall score improvement cannot hide breakage in critical queries. For subjective output qualities like tone or format adherence, the author recommends rubric-based model grading at zero temperature rather than string matching alone.