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Every workflow answers one practical question: can I trust these results before I rely on them? anyformat gives you two numbers to answer it — confidence and accuracy — and they do different jobs.
The short version: anyformat gives every value a confidence score so you know where to look — review the low-confidence ones first. As you verify documents, those verifications produce an accuracy number that tells you how often the workflow is actually right. Aim for high accuracy with light review on the low-confidence cases — you don’t need to check everything.

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Confidence

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What is confidence?

Confidence represents how certain anyformat is about an extracted value. It’s expressed as a percentage:
  • High confidence - the model is very sure
  • Low confidence - the value may be ambiguous or unclear
Confidence is calculated per:
  • Field
  • Document
  • Workflow (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 doesn’t 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 actually correct, based on the documents you’ve verified.
Accuracy reflects confirmed correctness, not how sure the model felt.
Accuracy is calculated 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. Sorting fields by confidence or accuracy quickly surfaces:
  • Fields that consistently fail
  • Fields that don’t need review anymore
  • 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 if needed
Accuracy improves through human feedback loops.
Refinement improves future documents, not past ones.

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A realistic quality goal

You don’t need 100% confidence or 100% accuracy. A good goal is:
High accuracy with focused human review on low-confidence cases.
That’s 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.