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

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 competitor documentation for API rate limits before the issues were caught. Post-mortem revealed the team had relied entirely on manual spot-checks, with no automated evaluation in place. In response, they built a production-grade LLM evaluation pipeline incorporating a suite of judges covering faithfulness, instruction-following, JSON schema validation, and domain accuracy. The system integrates with CI/CD workflows to block low-quality deployments and now catches 92% of hallucinations before code reaches production.