Confidence scores for Box Extract API: Know when to rely on your extractions

Box has introduced a confidence score feature to its AI-powered metadata extraction service, providing users with a probabilistic measure of extraction accuracy for each field. These scores, ranging from 0 to 1, indicate the likelihood of an extracted field being correct, aiding users in determining which extractions need human verification. The scores are generated through consistency analysis of responses from the language model, with high scores reflecting consistency across different prompts. Users can incorporate confidence scores into their workflows by including a specific parameter in their API requests, enabling them to programmatically route lower-confidence fields for manual review. While high-confidence scores suggest reliable extractions, they are not guarantees, and critical data should be cross-verified. The feature is currently supported by Google Gemini models and is limited to the /ai/extract_structured endpoint. Confidence scores are designed to optimize extraction workflows by prioritizing human oversight where necessary, making them particularly useful in high-volume scenarios where manual review of every field is impractical.

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