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

Short summary

A team shares how they replaced manual 'looks good to me' testing with an automated LLM evaluation pipeline that catches 92% of hallucinations before deployment. The system uses a judge ensemble (faithfulness, instruction following, JSON schema validation, safety, domain expert) running in CI/CD with versioned golden datasets. After a production incident where 500+ users saw hallucinated responses, the team built regression detection that blocks merges degrading quality.

• •Manual evaluation ('ask 5 questions, thumbs up') failed in production—500+ users saw hallucinated responses
• •Automated judge ensemble (faithfulness, instruction following, safety, domain expert) catches 92% of hallucinations pre-deployment
• •Evaluation runs in CI/CD with versioned golden datasets and regression detection that blocks degrading merges

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