How One Team Built Automated LLM Evaluation to Catch 92% of Hallucinations Pre-Deployment

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 exposed a critical gap: their entire testing process consisted of manually reviewing five sample questions with no automated checks. In response, the team built a production-grade evaluation pipeline integrating a suite of judges covering faithfulness, instruction-following, JSON schema validation, safety, and domain accuracy. The system was designed to run within CI/CD workflows, blocking code merges that degrade response quality and enabling rapid regression detection. The pipeline ultimately automated the catching of 92% of hallucinations before deployment, replacing subjective gut-checks with measurable, domain-specific metrics.