The original title is about building LLM evaluation pipelines. Let me rewrite it for a mobile feed.

Short summary

A practical guide to building production-grade LLM evaluation pipelines after a RAG customer support assistant hallucinated policies to 500+ users. The approach replaces manual 'looks good' testing with an automated harness using multiple LLM judges (faithfulness, instruction following, JSON schema, safety, domain expert) with defined thresholds. The system catches 92% of hallucinations pre-deployment and integrates into CI/CD to block quality-degrading merges.

• •Replaced manual review with automated judge ensemble catching 92% of hallucinations
• •Multiple judge types: faithfulness, instruction following, JSON schema, safety, domain expert
• •CI/CD integration blocks merges that degrade LLM output quality

Generated with AI, which can make mistakes.