A production-ready faithfulness judge outlines scoring for fidelity to context, using few-shot demonstrations to illustrate correctness and alignment.

Golden dataset strategy starts small, around 50 real production cases, stratified into basic, edge, adversarial, and multilingual/long-context categories, with versioning tracked in Git for traceability.

The golden dataset approach emphasizes harvesting failures as new test cases, ensuring the dataset grows as production issues arise and is version-controlled.

Initial golden set comprises roughly 50 production cases, organized by stratification and versioned, with ongoing dataset growth as issues surface in production.

Over six months of production evaluation, automation achieved a 92% automatic hallucination catch rate, dramatically reduced incidents from about 3 per month to roughly 0.2, and accelerated prompt iteration cycles.

The evaluation pipeline pushes test cases from a golden set into an LLM under test, with a judge ensemble assessing faithfulness, instruction follower quality, JSON schema compliance, and other metrics, and the results feed dashboards and performance metrics.

A simple regression-detection framework compares current scores against baselines for each judge, flagging regressions with straightforward statistical checks.

The results highlight faster iteration (from hours to minutes), significant reductions in production incidents, and the open-source tooling—llm-eval-harness, prompt-registry, and eval-dashboard—licensed under MIT to encourage adoption and extension.

A practical getting-started path is outlined: five-minute setup to install the harness, define test cases, build a judge ensemble, run evaluations, and integrate with CI, with a mindset shift toward treating evaluation as infrastructure.

CI/CD integration is demonstrated via GitHub Actions workflows that run evaluations on pull requests and nightly schedules, automatically collecting results, performing regressions, and posting concise summaries to PRs.

Open-source tooling is highlighted, featuring MIT-licensed components like llm-eval-harness, prompt-registry, and eval-dashboard, designed for production-grade evaluation pipelines, with quick-start steps and example code.

The overarching takeaway emphasizes treating evaluation as infrastructure—maintaining versioned judges and golden datasets as IP, with automated evaluation serving as the core reliability guarantee at scale.