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

Short summary

A practical guide to building automated LLM evaluation pipelines, motivated by a production incident where a RAG assistant hallucinated policies to 500+ users. The article covers domain-specific judge ensembles (faithfulness, instruction following, JSON schema, safety, domain expert), CI/CD integration to block degrading merges, and golden dataset management. Reports catching 92% of hallucinations before deployment.

• •Academic benchmarks don't validate your specific use case — production needs domain-specific judges
• •Judge ensemble includes faithfulness, instruction following, JSON schema, safety, and domain expert evaluations
• •Evaluation must run in CI/CD with regression detection to catch prompt-change degradations immediately

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