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

A software team discovered critical gaps in their AI evaluation process after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users. The system had incorrectly cited non-existent policies and pulled rate-limit figures from a competitor's documentation before anyone noticed. The incident prompted the team to replace their informal 'thumbs up' review method with a structured, automated evaluation pipeline integrated into their CI/CD workflow. Their solution uses a multi-judge ensemble assessing faithfulness, instruction-following, JSON schema validity, and domain accuracy against a versioned golden dataset. The pipeline now blocks code merges that degrade response quality, catching the vast majority of hallucinations before they reach production.