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

A development team discovered the cost of informal AI testing after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users before issues were caught. The assistant fabricated billing policies and cited competitor documentation as fact, exposing a complete absence of automated evaluation. In response, the team built a production-grade evaluation pipeline integrating multiple judge types — including faithfulness, instruction-following, JSON schema, and safety checks — into their CI/CD workflow. The system uses a versioned golden dataset and an ensemble of LLM-based and deterministic judges to catch regressions before deployment. According to the team, the new pipeline now detects 92% of hallucinations prior to production release.