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

Short summary

A practical guide to building automated LLM evaluation pipelines that replace manual 'looks good to me' reviews. The author shares a post-mortem where a RAG support assistant hallucinated policies to 500+ users due to zero automated eval. The article details a judge ensemble architecture (faithfulness, instruction following, JSON schema, safety, domain expert) with thresholds, CI/CD integration, and Python code for an evaluation harness.

• •Manual review failed: 500+ users saw hallucinated responses before issues were caught
• •Production eval needs domain-specific judges, CI/CD integration, and regression detection — not academic benchmarks
• •Judge ensemble covers faithfulness, instruction following, schema validation, safety, and domain accuracy with configurable thresholds

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