Build LLM eval pipelines that catch 92% of hallucinations pre-deployment

Short summary

A practical guide to building automated LLM evaluation pipelines that replaced manual 'looks good to me' reviews with a judge ensemble catching 92% of hallucinations pre-deployment. The pipeline uses domain-specific judges (faithfulness, instruction following, JSON schema, safety, domain expert) with defined thresholds, integrated into CI/CD to block quality regressions. Includes code for test case management, LLM judge implementation, and evaluation harness architecture.

• •Automated judge ensemble catches 92% of hallucinations before deployment
• •Five judge types: faithfulness, instruction following, JSON schema, safety, domain expert
• •Evaluation pipeline integrates into CI/CD to block merges that degrade quality

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