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

A development 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 issues were detected. The failures included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing a test process that relied entirely on manual spot-checks. In response, the team built a production-grade evaluation pipeline integrating automated judges for faithfulness, instruction-following, JSON schema validation, and safety into their CI/CD workflow. The system uses a versioned golden dataset and a multi-judge ensemble to score model responses at scale, blocking pull requests that degrade quality benchmarks. The approach replaced subjective human review with domain-specific, metric-driven evaluation capable of catching 92% of hallucinations before any code reaches deployment.