How One Team Built Automated LLM Eval Pipelines After Hallucinations Hit 500 Users

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 competitor data. The failures stemmed from a manual review process of just five questions with no automated evaluation in place. In response, the team designed a production-grade LLM evaluation pipeline that uses a ensemble of judges — covering faithfulness, instruction-following, JSON schema validation, and safety — integrated directly into CI/CD workflows. The system relies on domain-specific golden test datasets, regression detection, and LLM-as-judge scoring to catch issues before deployment. According to the team, the pipeline now detects 92% of hallucinations before code reaches production.