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Every workflow answers one practical question: can I trust these results before I rely on them? Two numbers answer it, confidence and accuracy. They do different jobs. The short version: anyformat gives every value a confidence score, so you know where to look. Review the low-confidence values first. Your verifications then produce an accuracy number that tells you how often the workflow is right. Aim for high accuracy with light review on the low-confidence cases. You do not check everything.

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Confidence

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What is confidence?

Confidence is how certain anyformat is about an extracted value. anyformat expresses it as a percentage:
  • High confidence: the model is very sure
  • Low confidence: the value may be ambiguous or unclear
anyformat calculates confidence per:
  • Field
  • Document
  • Workflow, as an average
Confidence score

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What confidence is (and isn’t)

Confidence IS

  • A signal, not a verdict
  • A way to prioritize human review
  • A guide for where to look first

Confidence is NOT

  • A guarantee of correctness
  • A replacement for verifying results yourself
  • A measure of business accuracy
A value can have:
  • High confidence and still be wrong
  • Low confidence and still be correct

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How to use confidence effectively

Use confidence to:
  • Focus review on low-confidence fields
  • Skip reviewing obviously reliable values
  • Reduce overall human effort
A good workflow does not eliminate low confidence. It contains it.

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Accuracy

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What is accuracy?

Accuracy measures how often extracted values are correct, over the documents you verified.
Accuracy reflects confirmed correctness, not how sure the model felt.
anyformat calculates accuracy from:
  • Fields you verified as correct
  • Fields you corrected

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Accuracy vs confidence

You need both:
  • Confidence to guide review
  • Accuracy to judge quality

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What accuracy tells you

Accuracy helps you answer:
  • Can I trust this workflow?
  • Is it ready to scale?
  • Which fields are fragile?
Low accuracy usually points to:
  • Ambiguous instructions
  • Poor field definitions
  • Edge cases in documents

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Per field

Both numbers are most useful per field. Sort fields by confidence or accuracy to surface:
  • Fields that consistently fail
  • Fields that no longer need review
  • Outliers dragging accuracy down

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Improving results

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How to improve confidence

To improve confidence:
  • Make instructions more explicit
  • Clarify where information appears
  • Reduce ambiguity in field definitions
  • Split complex fields into simpler ones
Confidence improves when field definitions become clearer.

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How to improve accuracy

To improve accuracy:
  • Correct wrong values while verifying
  • Review low-confidence fields carefully
  • Refine the workflow when patterns appear
  • Adjust fields or instructions
Accuracy improves through human feedback loops.
Refinement improves future documents, not past ones.

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A realistic quality goal

You do not need 100% confidence or 100% accuracy. A good goal is:
High accuracy with focused human review on low-confidence cases.
That is how anyformat scales without burning time.

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What’s next?

Verification & Review

Review and correct results: the loop that builds up accuracy.

Health

Measure quality deliberately with datasets and evaluations.