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

A software team discovered critical gaps in their AI quality process after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users before anyone noticed. The system had confidently cited non-existent policies and pulled rate-limit figures from a competitor's documentation, exposing a complete absence of automated evaluation. In response, the team built a production-grade evaluation pipeline integrating a suite of judges — covering faithfulness, instruction-following, JSON schema validation, and safety — into their CI/CD workflow. The pipeline uses a versioned golden dataset and an LLM judge ensemble to score responses automatically and block pull requests that degrade quality. The approach replaced informal manual review with structured, metric-driven evaluation capable of catching the majority of hallucinations before any code reaches production.