Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

Short summary

A team shares how they replaced manual 'looks good to me' testing with an automated LLM evaluation pipeline that catches 92% of hallucinations before deployment. The system uses a judge ensemble — faithfulness, instruction following, JSON schema validation, safety, and domain expert judges — running against a versioned golden dataset in CI/CD. The article includes code for the evaluation harness, judge implementations, and threshold configuration for blocking merges that degrade quality.

• •Manual evaluation ('ask 5 questions, thumbs up') let 500+ users see hallucinated responses before detection
• •Judge ensemble covers faithfulness, instruction following, JSON schema, safety, and domain-specific accuracy with configurable thresholds
• •Pipeline integrates into CI/CD to block merges that degrade quality, with regression detection across versioned golden datasets

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