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. 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 integrating domain-specific LLM judges, deterministic schema checks, and CI/CD regression detection into a structured harness. The system now flags hallucinations and instruction-following failures automatically before deployment, replacing informal review with measurable, repeatable quality gates.