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

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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 before the issue was caught. The incident, traced to a test process that amounted to reading a handful of answers and giving a thumbs up, prompted a complete overhaul of their evaluation approach. The team subsequently built a production-grade evaluation pipeline incorporating multiple automated judges covering faithfulness, instruction following, JSON schema validation, and safety checks. The system integrates directly into CI/CD workflows to block code merges that degrade response quality, and uses a versioned golden dataset for regression detection. According to the team, the automated pipeline now catches 92% of hallucinations before any changes reach deployment.

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