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 production architecture with multiple LLM judges (faithfulness, instruction following, JSON schema, safety, domain expert), golden dataset management, and CI/CD integration that caught 92% of hallucinations before deployment.

• •Academic benchmarks don't validate production use cases; build domain-specific judge ensembles instead
• •Architecture: golden test cases → LLM under test → judge ensemble → metrics & regression detection
• •Five judge types with defined thresholds: faithfulness (0.8), instruction following (0.9), JSON schema (1.0), safety (1.0), domain expert (0.85)

Generated with AI, which can make mistakes.