How One Team Built Automated LLM Evaluation That Catches 92% of Hallucinations

A software team discovered a critical gap in their AI quality process after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users. The system had cited non-existent policies and pulled rate-limit figures from a competitor's documentation before the errors were caught. The post-mortem revealed their entire testing workflow relied on manually reviewing a handful of answers with no automated checks. In response, the team built a production-grade evaluation pipeline integrating multiple judge types — including faithfulness, instruction-following, schema validation, and safety — directly into their CI/CD workflow. The new system uses a versioned golden dataset and an ensemble of LLM-based and deterministic judges to block low-quality model changes before they reach users.