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

A development team discovered the cost of informal AI testing after their RAG-based customer support assistant served hallucinated responses to over 500 users in production, citing non-existent policies and a competitor's documentation. The failures were traced to a near-absent evaluation process that relied on manually reviewing just five sample questions before deployment. In response, the team built a production-grade LLM evaluation pipeline integrating a suite of automated judges covering faithfulness, instruction following, JSON schema validation, and safety checks. The system runs within CI/CD workflows, enabling regression detection and blocking code merges that degrade response quality. According to the team, the new pipeline now catches 92% of hallucinations before any changes reach production.