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 chatbot hallucinated policies and cited a competitor's documentation, reaching over 500 users before the errors were caught. The team's previous evaluation process consisted of manually reviewing just five questions before deployment, with no automated checks in place. In response, they built a production-grade evaluation pipeline that integrates into CI/CD workflows and uses a multi-judge ensemble to assess faithfulness, instruction-following, JSON schema validity, and domain accuracy. The system uses LLM-based judges with structured scoring, few-shot examples, and deterministic validators, each with defined pass thresholds. The new pipeline reportedly catches 92% of hallucinations before deployment, blocking pull request merges that degrade response quality.