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

Short summary

The author shares a real-world failure where a RAG customer-support assistant hallucinated policies to 500+ users because testing relied on manual 'looks good to me' checks. They then built an automated evaluation pipeline using LLM-as-judge ensembles (faithfulness, instruction following, safety, domain expert), golden dataset stratification, and CI/CD integration to block quality regressions. The post includes Python code for the evaluation harness, judge implementations, and practical guidance on starting with 50 real production cases and growing from there.

• •Manual vibe-checks missed hallucinations that reached 500+ production users; automated eval pipelines caught 92% before deployment
• •Judge ensembles combine faithfulness, instruction-following, JSON schema, safety, and domain-specific LLM judges with configurable thresholds
• •Start with 50 real production cases stratified across happy-path, edge cases, adversarial, and multilingual; version in Git and add every production failure as a new test case

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