Build production-grade LLM evaluation pipelines from vibes to metrics

Short summary

A practical guide to building automated LLM evaluation pipelines that replace manual 'looks good' reviews with structured judge ensembles. Covers faithfulness, instruction-following, JSON schema validation, safety, and domain-expert judges with configurable thresholds. Includes Python code for a test harness, LLM judge implementation, and CI/CD integration to block merges that degrade quality, claiming 92% hallucination catch rate.

• •Replace manual review with automated judge ensembles (faithfulness, instruction-following, safety, domain-expert)
• •Golden datasets must be versioned, stratified, and integrated into CI/CD to catch regressions
• •Claims 92% hallucination detection rate before deployment using LLM-as-judge approach

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