Dev Team Builds Automated LLM Eval Pipeline After Hallucinations Reached 500 Users

A software 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 before the issue was caught. The team had relied solely on manual spot-checking, asking a handful of questions and approving outputs by intuition rather than structured evaluation. In response, they built a production-grade evaluation pipeline integrating a suite of automated judges covering faithfulness, instruction-following, JSON schema validation, and safety checks. The system is designed to run within CI/CD workflows, blocking code merges that degrade response quality and enabling rapid regression detection. According to the team, the new pipeline now catches 92% of hallucinations before deployment, replacing subjective review with measurable, domain-specific metrics.