The original title is about building LLM evaluation pipelines. Let me rewrite it to be punchy and informative while keeping key facts.

Short summary

A practical guide to building automated LLM evaluation pipelines that replace manual 'looks good to me' reviews with measurable metrics. The author shares a post-mortem where 500+ users saw hallucinated responses due to lack of automated eval, then details a judge-based harness covering faithfulness, instruction following, JSON schema validation, safety, and domain expertise. Includes Python code for an evaluation harness with LLM judges, threshold-based pass/fail, and CI/CD integration.

• •Manual review ('vibes-based eval') missed hallucinations affecting 500+ users in production
• •Judge ensemble covers faithfulness, instruction following, schema validation, safety, and domain accuracy
• •Pipeline integrates into CI/CD to block merges that degrade quality, catching 92% of hallucinations

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