Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

Short summary

A practical guide to building production-grade LLM evaluation pipelines, motivated by a real incident where a RAG support assistant hallucinated policies to 500+ users. Covers judge ensembles (faithfulness, instruction following, JSON schema, safety, domain expert), golden dataset management, CI/CD integration, and regression detection. Includes Python code for the evaluation harness and LLM judge implementations.

• •Judge ensemble approach: faithfulness, instruction-following, JSON schema, safety, and domain-specific LLM judges with configurable thresholds
• •Golden datasets must be versioned, stratified, and integrated into CI/CD to block quality regressions
• •Academic benchmarks like MMLU don't validate production use cases; domain-specific evaluation is essential

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