> ## Documentation Index > Fetch the complete documentation index at: https://docs.anyformat.ai/llms.txt > Use this file to discover all available pages before exploring further. # Analytics & Quality > Understanding and improving processing quality over time 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. *** ## Confidence ### 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 *** ### What confidence is (and isn't) * A **signal**, not a verdict * A way to prioritize human review * A guide for where to look first * 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 *** ### 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**. *** ## Accuracy ### 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 *** ### Accuracy vs confidence | Confidence | Accuracy | | ------------------------------------------------ | --------------------------------- | | How sure the model is | How often it is confirmed right | | Available immediately, before you check anything | Builds up as you verify documents | | A guess about each value, up front | A track record, after the fact | | Helps you prioritize what to review | Measures real performance | You need **both**: * Confidence to guide review * Accuracy to judge quality *** ### 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 *** ### 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 *** ## Improving results ### 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. *** ### 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. *** ## 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. *** ## What's next? Review and correct results: the loop that builds up accuracy. Measure quality deliberately with datasets and evaluations.