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. The assistant had incorrectly cited non-existent policies and pulled rate-limit figures from a competitor's documentation before the issue was caught. A post-mortem revealed the team had relied entirely on manual spot-checking, with no automated evaluation in place. In response, they built a production-grade evaluation pipeline incorporating domain-specific LLM judges, deterministic schema validators, and CI/CD integration to block code merges that degrade output quality. The system uses a versioned golden dataset and a multi-judge ensemble assessing faithfulness, instruction-following, safety, and domain accuracy before any model change reaches production.