The original title is "Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics"

Short summary

A team shares how they built a production-grade LLM evaluation pipeline after a RAG assistant hallucinated policies to 500+ users. The system uses domain-specific LLM judges (faithfulness, instruction following, safety, JSON schema validation) with thresholds, golden datasets, and CI/CD integration to catch 92% of hallucinations pre-deployment. Includes code for a modular evaluation harness with judge ensembles and regression detection.

• •RAG assistant shipped with zero automated evaluation, causing 500+ hallucinated responses in production
• •Built judge ensemble covering faithfulness, instruction following, JSON schema, safety, and domain expertise
• •Pipeline catches 92% of hallucinations before deployment via CI/CD integration and versioned golden datasets

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