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

Short summary

A team shares how they replaced manual 'looks good to me' testing with an automated LLM evaluation pipeline that catches 92% of hallucinations before deployment. The system uses a judge ensemble covering faithfulness, instruction following, JSON schema validation, safety, and domain expertise, integrated into CI/CD to block degrading merges. Includes Python code for the evaluation harness, judge implementations, and golden dataset management.

• •Automated LLM evaluation pipeline catches 92% of hallucinations pre-deployment
• •Judge ensemble covers faithfulness, instruction following, schema validation, safety, and domain expertise
• •Integrated into CI/CD to block merges that degrade quality, with regression detection

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