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

A software 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 a test process that relied entirely on manual spot-checks. In response, the team built a production-grade evaluation pipeline integrating automated judges for faithfulness, instruction-following, JSON schema validation, and domain accuracy into their CI/CD workflow. The system uses an ensemble of LLM-based and deterministic judges with defined score thresholds to block code merges that degrade response quality. The approach replaces informal human review with versioned golden datasets and regression detection, enabling systematic quality control before any model changes reach production.