How One Team Built Automated LLM Evaluation to Catch 92% of Hallucinations Pre-Deployment

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 system incorrectly cited non-existent policies and surfaced documentation from a competitor, exposing the team's reliance on informal, manual testing. In response, they built a production-grade evaluation pipeline that integrates automated judges into CI/CD workflows to block degraded model outputs before release. The pipeline uses a combination of deterministic checks and LLM-based judges covering faithfulness, instruction-following, schema validation, and domain accuracy. The team reports the system now catches 92% of hallucinations prior to deployment, replacing subjective review with measurable, regression-aware metrics.