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

Short summary

The article describes building a production-grade LLM evaluation pipeline after a RAG support assistant hallucinated policies to 500+ users because testing was manual vibe-checks. It presents a judge ensemble architecture combining faithfulness, instruction-following, JSON schema validation, safety, and domain-expert evaluators with defined thresholds. The pipeline integrates into CI/CD to block merges that degrade quality, replacing ad-hoc 'looks good to me' reviews with automated, versioned golden-dataset testing.

• •Manual vibe-check testing let hallucinated responses reach 500+ production users
• •Judge ensemble covers faithfulness, instruction-following, schema validation, safety, and domain accuracy with per-judge thresholds
• •Pipeline integrates into CI/CD to block quality-degrading merges and detect regressions from prompt changes

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