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

A development team discovered the cost of informal AI testing after their RAG-based customer support assistant delivered hallucinated responses to over 500 users in production. The errors included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing a complete absence of automated evaluation. In response, the team built a production-grade LLM evaluation pipeline that replaces ad-hoc review with structured, automated checks integrated into CI/CD workflows. The system uses a multi-judge ensemble assessing faithfulness, instruction-following, JSON schema validity, safety, and domain accuracy against a versioned golden dataset. The pipeline now catches 92% of hallucinations before deployment, blocking pull requests that degrade quality and enabling regression detection when prompts change.