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

Short summary

A team shares how they moved from manual 'looks good to me' testing to an automated LLM evaluation pipeline that catches 92% of hallucinations pre-deployment. The system uses a judge ensemble (faithfulness, instruction following, JSON schema validation, safety, domain expert) with configurable thresholds, integrated into CI/CD to block quality-degrading merges. Includes code for the evaluation harness and LLM judge implementation.

• •Judge ensemble covers faithfulness, instruction following, JSON schema, safety, and domain-specific accuracy with per-judge thresholds
• •Evaluation runs in CI/CD to block merges that degrade quality, replacing manual review
• •Golden datasets are versioned, stratified, and growing — academic benchmarks like MMLU don't reflect production needs

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