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

Short summary

A detailed walkthrough of building production-grade LLM evaluation pipelines, motivated by a real incident where a RAG assistant hallucinated responses to 500+ users. The article presents an evaluation harness with multiple judge types (faithfulness, instruction following, JSON schema, safety, domain expert) and shows code for LLM-based judges using structured outputs. It emphasizes CI/CD integration, golden dataset management, and regression detection over academic benchmarks.

• •Production LLM eval needs domain-specific judges, CI/CD integration, and regression detection—not academic benchmarks
• •Judge ensemble includes faithfulness, instruction following, JSON schema validation, safety, and domain expert checks
• •Code examples show a typed evaluation harness with async concurrency and structured LLM judge outputs

Generated with AI, which can make mistakes.