The original title is about building production-grade LLM evaluation pipelines. Let me rewrite this for a mobile feed.

Short summary

A practical guide to replacing manual 'looks good to me' LLM testing with automated evaluation pipelines that caught 92% of hallucinations before deployment. The system uses a judge ensemble combining RAGAS faithfulness scores, instruction-following checks, JSON schema validation, safety filters, and domain-specific expert judges. Evaluation runs in CI/CD with regression detection, golden dataset management, and PR-level metric reporting.

• •Multi-judge ensemble: faithfulness, instruction following, JSON schema, safety, domain expert
• •Automated pipeline catches 92% of hallucinations before deployment with CI/CD integration
• •Golden dataset management with versioned, stratified test cases and regression detection

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