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 policy details and surfaced rate-limit figures from a competitor's documentation, exposing the risk of relying solely on informal, manual review. In response, the team built a production-grade evaluation pipeline integrating multiple automated judges — covering faithfulness, instruction-following, JSON schema validation, and safety — into their CI/CD workflow. The pipeline uses a versioned 'golden dataset' of test cases and runs evaluations on every pull request to block merges that degrade output quality. The new system reportedly catches 92% of hallucinations before deployment, replacing what the team described as a 'thumbs up' review process with measurable, domain-specific metrics.