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 the issue was detected. The failures included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing the team's reliance on informal, manual testing. In response, they built a production-grade evaluation pipeline integrating multiple automated judges covering faithfulness, instruction-following, JSON schema validation, and domain-specific accuracy. The system is designed to run within CI/CD workflows, blocking code merges that degrade response quality and enabling rapid regression detection. The resulting pipeline now catches 92% of hallucinations before deployment, replacing subjective human review with structured, metrics-driven assessment.