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 retrieval-augmented generation customer support assistant that served hallucinated responses to over 500 users. The failures included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing the risks of relying solely on informal manual testing. In response, the team built a production-grade evaluation pipeline that runs automatically within CI/CD workflows, using a ensemble of judges to assess faithfulness, instruction-following, schema validity, and safety. The system uses domain-specific LLM judges alongside deterministic checks, with configurable pass thresholds and a versioned golden dataset for regression detection. The approach replaced ad-hoc review with structured, automated metrics capable of blocking low-quality model updates before they reach production.