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 system had fabricated a non-existent billing policy and surfaced rate-limit figures from a competitor's documentation, exposing the risks of purely manual review. The post-mortem revealed their entire testing process consisted of asking five questions and eyeballing the answers, with no automated safeguards in place. The team subsequently built a production-grade evaluation pipeline incorporating a suite of judges covering faithfulness, instruction-following, JSON schema validation, and safety checks integrated directly into their CI/CD workflow. The new framework, designed around domain-specific golden test datasets and regression detection, reportedly catches 92% of hallucinations before any code reaches deployment.