Feedback Loop

What is a Feedback Loop in Intelligent Document Processing (IDP)?

A feedback loop in IDP is the mechanism by which human corrections and validation outcomes are captured and used to improve automated processing accuracy over time.

When a human reviewer corrects an extraction error, that correction signals to the system that the AI model's output was inaccurate for that input. Corrections can include:

• Adjusting a misread value
• Reclassifying a document
• Overriding a validation flag

How feedback loops drive continuous improvement

Feedback loops enable continuous model improvement without requiring periodic manual retraining campaigns. Corrections made during normal exception handling accumulate as training signal, and models are updated to reflect patterns in those corrections.

What improves over time

As feedback accumulates, the system adapts in measurable ways:

• Document types and fields that initially required frequent human review achieve higher confidence scores
• Exception rates decrease as the model learns from recurring correction patterns
• The system adapts to the specific document variations, layouts, and content patterns of a given organization's workflows

Why this matters in production

This learning mechanism is what allows intelligent document processing (IDP) systems to improve accuracy in production. Rather than relying on scheduled retraining, the system continuously incorporates real-world corrections, making it more accurate and better aligned to the organization's specific documents over time.