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

A development team building a RAG-based customer support assistant discovered critical gaps in their testing process only after over 500 users received hallucinated responses in production. The assistant had confidently cited non-existent policies and pulled rate-limit figures from a competitor's documentation, exposing the dangers of informal, manual review. In response, the team built a production-grade LLM evaluation pipeline integrating automated judges for faithfulness, instruction-following, JSON schema validation, and domain accuracy. The system was designed to run within CI/CD pipelines, blocking code merges that degrade response quality and enabling rapid regression detection. The approach replaced subjective human review with measurable, versioned evaluation metrics, reportedly catching 92% of hallucinations before deployment.