Dev Team Builds Automated LLM Eval Pipeline After 500 Users Saw 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, including fabricated billing policies and competitor API data. Their previous evaluation process consisted of manually reviewing just five questions before deployment, with no automated checks in place. In response, the team built a production-grade LLM evaluation pipeline integrating multiple judge types — covering faithfulness, instruction-following, JSON schema validation, and safety — into their CI/CD workflow. The system uses a versioned golden dataset and a judge ensemble to catch regressions automatically before any code merge. According to the team, the new pipeline now detects 92% of hallucinations before deployment, replacing subjective gut-checks with measurable, domain-specific metrics.