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

Short summary

Duplicate of an earlier post on building production-grade LLM evaluation pipelines. A team replaced manual 'looks good' testing with an automated LLM judge ensemble (faithfulness, instruction following, JSON schema, safety, domain expert) that catches 92% of hallucinations pre-deployment. Includes Python code for the evaluation harness, judge abstractions, and GPT-4o-mini integration via instructor.

• •Replaced manual testing with automated LLM judge ensemble catching 92% of hallucinations
• •Five judge types with configurable thresholds for production evaluation
• •Python code for evaluation harness, judge classes, and CI/CD integration

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