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

A development 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. The assistant had fabricated a billing policy and cited a competitor's API rate limits, 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 structured evaluation pipeline incorporating multiple judge types — including faithfulness, instruction-following, JSON schema validation, and safety checks — integrated directly into their CI/CD workflow. The system uses a versioned golden dataset and an ensemble of LLM-based and deterministic judges to block deployments that degrade response quality.