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 automated judges that assess faithfulness, instruction-following, schema validity, safety, and domain accuracy. The system runs within CI/CD workflows, blocking code merges that degrade response quality and detecting regressions immediately after prompt changes. The team also introduced versioned golden datasets and a judge ensemble architecture to replace subjective review with measurable, domain-specific metrics.