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

Short summary

A practical guide to building automated LLM evaluation pipelines after a production incident where a RAG assistant hallucinated policies to 500+ users. Covers domain-specific judge ensembles (faithfulness, instruction following, JSON schema, safety, domain expert), CI/CD integration to block degrading merges, and golden dataset management. Includes Python code for the evaluation harness and LLM judge implementations.

• •Production eval requires domain-specific judges, CI/CD speed, regression detection, and versioned golden datasets — not academic benchmarks
• •Judge ensemble includes faithfulness, instruction following, JSON schema validation, safety, and domain expert checks with defined thresholds
• •Code provided for evaluation harness architecture with controlled concurrency and structured LLM judge outputs

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