How One Team Built Automated LLM Evaluation That Catches 92% of Hallucinations

A software team discovered the cost of informal AI testing after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users before any issues were caught. The assistant fabricated a billing policy and pulled API rate-limit figures from a competitor's documentation, exposing a complete absence of automated evaluation. In the post-mortem, the team identified that academic benchmarks like MMLU are insufficient for production use cases and that domain-specific, automated evaluation pipelines are essential. They subsequently built a CI/CD-integrated evaluation system using a judge ensemble that scores responses on faithfulness, instruction-following, schema validity, and safety. The resulting pipeline now catches 92% of hallucinations before deployment by running versioned test cases against multiple automated judges with defined pass/fail thresholds.