Claude Architect Lab

Human Review Workflow Design

Core

Design human review workflows and confidence calibration · Difficulty 3/5

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When automating document extraction or analysis, human review workflows must be designed to catch errors that aggregate metrics hide.

The Hidden Risk of Aggregate Metrics

97% overall accuracy sounds excellent, but may mask:

  • 85% accuracy on handwritten documents
  • 60% accuracy on a specific field (e.g., dates in non-standard formats)
  • 100% accuracy on common document types that dominate the dataset

    • Stratified Random Sampling

    Don't just sample randomly -- stratify by:

  • Document type
  • Field type
  • Confidence level
  • Source characteristics

    • This detects poor performance in specific segments that random sampling might miss, and catches novel error patterns as document types evolve.

    Routing Strategy

    Prioritize limited reviewer capacity by routing to human review:

  • Extractions with low model confidence
  • Ambiguous or contradictory source documents
  • Document types with historically higher error rates
  • New document types not well-represented in training data

    • Key Takeaways

    • ✓Aggregate accuracy metrics can mask poor performance on specific segments
    • ✓Stratified sampling by document type and field catches hidden errors
    • ✓Route low-confidence and ambiguous extractions to human review

    Related Concepts

    Confidence Calibration & Review Thresholds

    Calibrate confidence thresholds using labeled validation sets, not intuition