How One Team Built Automated LLM Evaluation After 500 Users Got Hallucinated Answers

A development team discovered the limits of manual LLM testing after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users in production. The assistant fabricated a billing policy and cited competitor documentation for API rate limits before the issue was caught. The team found that informal review — asking a handful of questions and approving answers by feel — provided no reliable safety net against such failures. In response, they built a production-grade evaluation pipeline combining domain-specific LLM judges, deterministic schema checks, and CI/CD integration to automatically flag regressions before deployment. The system now catches an estimated 92% of hallucinations prior to release, using a versioned golden dataset and a judge ensemble covering faithfulness, instruction-following, safety, and domain accuracy.