How One Team Built Automated LLM Eval Pipelines After 500 Users Got Hallucinated Answers

A development team discovered the cost of informal AI testing after their RAG-based customer support assistant served hallucinated responses—including fabricated policies and competitor data—to over 500 users in production. The incident, which their post-mortem attributed to a test process of asking just five questions and eyeballing results, prompted a complete overhaul of their evaluation approach. The team built a production-grade LLM evaluation pipeline incorporating domain-specific judges, golden dataset management, and CI/CD integration to block low-quality code merges. Their judge ensemble assesses outputs across dimensions such as faithfulness, instruction following, JSON schema validity, and safety, each with defined score thresholds. The automated system now catches 92% of hallucinations before deployment, replacing subjective gut-feel reviews with measurable, repeatable metrics.