The original title is about building LLM evaluation pipelines, moving from manual testing to metrics. Let me rewrite this for a mobile feed.

Short summary

A team shares their journey from manual 'looks good to me' testing to a production-grade LLM evaluation pipeline that catches 92% of hallucinations pre-deployment. The article details a judge ensemble architecture combining faithfulness, instruction-following, JSON schema validation, safety, and domain-expert evaluators with configurable thresholds. It includes Python code for the evaluation harness, judge implementations using GPT-4o-mini, and integration patterns for CI/CD regression detection.

• •Manual LLM testing fails in production — the team's RAG assistant served 500+ users hallucinated responses before issues were caught
• •A judge ensemble architecture (faithfulness, instruction-following, schema, safety, domain-expert) with per-judge thresholds catches 92% of hallucinations
• •Pipeline integrates into CI/CD with golden datasets, regression detection, and PR comments to block quality-degrading merges

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