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

A software team discovered critical gaps in their AI quality process after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users before detection. The system had fabricated a non-existent billing policy and cited a competitor's API rate limit figures, exposing the team's reliance on informal, manual testing. In a post-mortem, the team identified that academic benchmarks like MMLU are insufficient for production use cases and that domain-specific, automated evaluation is essential. They subsequently built a CI/CD-integrated evaluation pipeline using a judge ensemble that tests for faithfulness, instruction-following, JSON schema validity, and safety. The new framework, detailed in a technical write-up, now catches 92% of hallucinations before deployment by running versioned test cases through automated LLM and deterministic judges.