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

A development team discovered critical gaps in their AI evaluation 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 relying solely on informal, manual testing. The team's post-mortem revealed their entire test process consisted of asking five questions and approving answers by gut feel, with no automated checks in place. In response, they built a production-grade evaluation pipeline featuring domain-specific LLM judges, deterministic schema validators, and CI/CD integration to block deployments that degrade quality. The new system uses a structured judge ensemble measuring faithfulness, instruction-following, safety, and domain accuracy, and has reportedly caught 92 percent of hallucinations before they reach production.