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

Short summary

A team shares how they built a production-grade LLM evaluation pipeline after a RAG support assistant hallucinated policies to 500+ users. The system uses a judge ensemble — faithfulness, instruction following, JSON schema validation, safety, and domain-specific expert judges — running in CI/CD with controlled concurrency. It catches 92% of hallucinations before deployment and provides regression detection when prompts or models change. The article includes code for the evaluation harness, judge abstractions, and golden dataset management.

• •Production LLM eval pipeline using a judge ensemble: faithfulness, instruction following, JSON schema, safety, domain expert
• •Catches 92% of hallucinations pre-deployment; runs in CI/CD to block quality regressions
• •Includes code for evaluation harness, LLM judge abstraction, and golden dataset management

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