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 errors included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing the dangers of informal, manual testing. In response, the team built a production-grade evaluation pipeline using a suite of automated judges assessing faithfulness, instruction-following, schema validity, safety, and domain accuracy. The system integrates with CI/CD workflows to block code merges that degrade model quality, replacing ad-hoc review with versioned golden datasets and regression detection. The approach reportedly catches 92% of hallucinations before deployment, offering a replicable framework for teams running LLMs in production.