Build production-grade LLM evaluation pipelines from vibes to metrics

Short summary

A practical guide to building production-grade LLM evaluation pipelines, motivated by a real incident where 500+ users saw hallucinated responses from a RAG support assistant. Covers domain-specific judge ensembles (faithfulness, instruction following, JSON schema, safety, domain expert), golden dataset management, and CI/CD integration. Includes Python code for the evaluation harness and LLM judge implementations.

• •Replaced manual 'looks good to me' testing with automated judge ensembles catching 92% of hallucinations
• •Five judge types: faithfulness, instruction following, JSON schema, safety, and domain expert with specific thresholds
• •Pipeline integrates into CI/CD to block merges that degrade quality, with dashboard and PR comment outputs

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