How One Team Built Automated LLM Eval Pipelines to Catch 92% of Hallucinations

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A development team discovered critical gaps in their AI evaluation process after deploying a RAG-based customer support assistant that served hallucinated responses to over 500 users. The system had no automated checks, relying solely on manual review of a handful of questions before release. In response, the team built a production-grade evaluation pipeline incorporating domain-specific LLM judges, deterministic schema validators, and regression detection integrated directly into their CI/CD workflow. The pipeline uses a 'golden dataset' of versioned test cases run against a ensemble of judges measuring faithfulness, instruction-following, safety, and domain accuracy. The approach reportedly catches 92% of hallucinations before deployment, blocking pull requests that degrade model output quality.

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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 before the issue was detected. The failures included fabricated billing policies and rate-limit figures pulled from a competitor's documentation, exposing the team's reliance on informal, manual testing. In response, they built a production-grade evaluation pipeline featuring a suite of automated judges assessing faithfulness, instruction-following, schema validity, safety, and domain accuracy. The system integrates directly into CI/CD workflows, blocking code merges that degrade output quality and enabling regression detection in real time. The team reports the pipeline now catches 92% of hallucinations before deployment, replacing subjective human review with structured, metric-driven evaluation.

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How One Team Built Automated LLM Eval Pipelines to Catch 92% of Hallucinations

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