How One Team Built Automated LLM Evals After Hallucinations Reached 500 Users

A development team discovered critical gaps in their AI evaluation process after a RAG-based customer support assistant served hallucinated responses to over 500 users in production. The assistant had falsely cited non-existent policies and pulled rate-limit figures from a competitor's documentation, exposing a complete absence of automated testing. The team's prior process consisted of manually reviewing just five sample questions before deployment, with no CI/CD integration or regression detection. In response, they built a production-grade evaluation pipeline using a judge ensemble that tests for faithfulness, instruction-following, JSON schema validity, and domain accuracy. The new system, integrated into their CI/CD workflow, now catches 92% of hallucinations before any code reaches deployment.