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

Short summary

A team shares how they built a production-grade LLM evaluation pipeline after a RAG chatbot hallucinated policies to 500+ users in production. The system uses domain-specific LLM judges (faithfulness, instruction following, safety, JSON schema validation) with thresholds, golden datasets, and CI/CD integration to block merges that degrade quality. The pipeline caught 92% of hallucinations before deployment, replacing manual 'looks good to me' reviews with automated, versioned evaluation.

• •RAG chatbot hallucinated to 500+ users due to zero automated evaluation
• •Built judge ensemble with faithfulness, instruction-following, safety, and domain-specific criteria
• •Pipeline integrated into CI/CD catches 92% of hallucinations pre-deployment

Generated with AI, which can make mistakes.