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 detection. 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 domain-specific LLM judges, deterministic schema checks, and safety filters into their CI/CD workflow. The system uses a versioned 'golden dataset' of test cases run against a judge ensemble measuring faithfulness, instruction-following, JSON validity, and domain accuracy. The automated pipeline now catches 92% of hallucinations before deployment, replacing ad-hoc review with measurable, regression-aware quality gates.