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 chatbot hallucinated policies to 500+ users. The pipeline uses domain-specific LLM judges (faithfulness, instruction following, JSON schema, safety, domain expert) with thresholds, golden datasets, and CI/CD integration to catch 92% of hallucinations pre-deployment. Includes Python code for the evaluation harness, judge abstractions, and integration with GPT-4o-mini via instructor.

• •Replaced manual 'looks good' testing with automated LLM judge ensemble catching 92% of hallucinations
• •Defines five judge types: faithfulness, instruction following, JSON schema, safety, and domain expert with configurable thresholds
• •Includes Python code for evaluation harness, judge classes, and CI/CD integration to block quality regressions

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