How One Dev Team Built Automated LLM Evals After 500 Users Got Hallucinated Answers

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 test process that relied entirely on manual review of just five questions, with no automated evaluation in place. In response, the team built a production-grade LLM evaluation pipeline integrating domain-specific judges, CI/CD blocking, and a versioned golden dataset for regression detection. The pipeline uses a judge ensemble assessing faithfulness, instruction-following, JSON schema validity, safety, and domain accuracy, catching 92% of hallucinations before deployment. The approach, shared on DEV Community, argues that academic benchmarks like MMLU are insufficient for real-world use cases and that automated evaluation must run at the speed of modern software delivery workflows.