How One Dev Team Cut LLM Hallucinations 92% With Automated Eval Pipelines

A development team building a RAG-based customer support assistant discovered the cost of informal testing after over 500 users received hallucinated responses in production, including fabricated billing policies and competitor API data. The incident exposed a complete absence of automated evaluation, with their pre-launch process amounting to reading a handful of answers and giving a thumbs up. In response, the team built a production-grade LLM evaluation pipeline incorporating domain-specific judges, CI/CD integration, and a versioned golden dataset for regression detection. The system uses a judge ensemble to assess faithfulness, instruction-following, JSON schema validity, and safety, catching 92% of hallucinations before deployment. The team argues that academic benchmarks like MMLU are insufficient for real-world use cases and that evaluation must be fast, automated, and embedded directly into the development workflow.