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7/20/2026
Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

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

Short summary

A practical guide to replacing manual 'vibes-based' LLM testing with automated evaluation pipelines that catch 92% of hallucinations pre-deployment. Covers building a judge ensemble (faithfulness, instruction-following, JSON schema, safety, domain expert), golden dataset management, and CI/CD integration to block quality regressions. Includes Python code for the evaluation harness, LLM judge implementations, and threshold configuration tables.

  • •Automated judge ensemble (faithfulness, instruction-following, safety, domain expert) replaces manual review
  • •Golden datasets must be versioned, stratified, and integrated into CI/CD to block degrading merges
  • •RAGAS covers basics but production needs domain-specific judges with configurable thresholds

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
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Build LLM eval pipelines that catch 92% of hallucinations pre-deployment
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Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics
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Build production-grade LLM evaluation pipelines from vibes to metrics
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The original title is about building production-grade LLM evaluation pipelines. Let me rewrite this for a mobile feed.
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