The original title is "Building 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 to me' reviews. The author shares a post-mortem where 500+ users saw hallucinated responses due to lack of automated eval, then details a judge-based harness with faithfulness, instruction-following, JSON schema, safety, and domain-expert evaluators. Includes Python code for the evaluation framework and CI/CD integration patterns.

• •Manual review ('ask 5 questions, thumbs up') fails in production — 500+ users saw hallucinated answers
• •Production eval needs domain-specific judges, CI/CD integration, regression detection, and versioned golden datasets
• •Judge ensemble includes faithfulness, instruction following, JSON schema validation, safety, and domain expert checks

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