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

Short summary

A team shares their journey from manual 'looks good to me' testing to automated LLM evaluation pipelines that catch 92% of hallucinations before deployment. The system uses domain-specific LLM judges for faithfulness, instruction following, JSON schema validation, safety, and domain expertise, integrated into CI/CD to block quality regressions. Includes Python code for the evaluation harness, judge implementations, and golden dataset management.

• •Replaced manual review with automated judge ensemble catching 92% of hallucinations pre-deployment
• •Five judge types: faithfulness, instruction following, JSON schema, safety, and domain expert with set thresholds
• •CI/CD integration blocks merges that degrade quality; golden datasets are versioned and stratified

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