agentsop-regression-gate

To block LLM regressions in CI, you enforce a held-out evaluation gate that runs a frozen golden eval set on every prompt edit, model swap, or RAG retriever change, blocking merges when score drops are detected.

A noise-aware threshold for LLM regression testing applies an absolute floor and a relative no-regression delta calibrated above measured evaluation noise, ensuring flaky evals do not trigger false CI failures.

Yes, the CI regression gate assembles discipline across common eval stacks like promptfoo, LangSmith, DSPy, and LlamaIndex, allowing teams to implement consistent regression gating across these frameworks.

Setting up an evaluation gate for RAG changes requires freezing golden examples, consuming a calibrated metric, defining an explicit threshold policy, and configuring CI failure behavior to catch statistical behavior shifts before release.

You should use a held-out eval set to prevent model behavior shifts whenever applying prompt edits, model or provider swaps, and RAG reranker modifications where statistical behavior changes must be caught before release.