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Workplace adoption · Guide

# Document AI: verify extracted fields before drafting the brief

Check OCR values, table relationships and source locations before AI uses a document in a business brief. Treat confidence as review evidence, not approval.

[Skills for Business](https://skillsforbusiness.ai/blog/editorial/)31 October 20267 min read

On this page

1.  [Recognizing text and understanding its placement are different tasks](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#recognizing-text-and-understanding-its-placement-are-different-tasks)
2.  [Keep source evidence attached to important fields](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#keep-source-evidence-attached-to-important-fields)
3.  [Check the relationships in a fictional supplier quote](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#check-the-relationships-in-a-fictional-supplier-quote)
4.  [Use confidence to prioritize review, with its limits visible](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#use-confidence-to-prioritize-review-with-its-limits-visible)
5.  [Evaluate extraction before evaluating the final brief](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#evaluate-extraction-before-evaluating-the-final-brief)
6.  [Retain corrected values and the reason they changed](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#retain-corrected-values-and-the-reason-they-changed)
7.  [Sources](https://skillsforbusiness.ai/blog/document-ai-extracted-fields-source-checks/#sources)

What to take away

-   Preserve each important extracted value with its document, page and supporting region.
-   Check row, column, unit and label relationships as well as recognized characters.
-   Calibrate confidence-based review on representative documents, and keep missing scores distinct from high confidence.

Before asking AI to turn a scanned document into a business brief, check the extracted fields that the brief will depend on. Keep each important value linked to its source page and region, inspect its relationship to labels and neighboring cells, and preserve uncertainty until someone or something can resolve it. A fluent summary cannot repair an incorrect quantity that entered the workflow earlier.

For operations teams, the decision is where extracted data can proceed automatically and where it needs review before becoming an asserted fact. This guide proposes a field-level checking method and a fictional supplier-quote example. We have not tested an extraction service, processed customer documents or run an independent model comparison for this article.

## Recognizing text and understanding its placement are different tasks

Optical character recognition, or OCR, converts visible text into machine-readable text. A document workflow may also identify fields, reconstruct tables and associate values with headings. Reading the characters correctly does not establish that a number belongs to the right product or that an unchecked box represents a selected option.

The [June 2025 revision of OCRBench v2](https://arxiv.org/html/2501.00321v2) evaluates visual text tasks extending beyond recognition, including localization, extraction, parsing and reasoning. Its analysis identifies difficulties involving layout and complex elements. The benchmark covers English and Chinese text-centric images, including challenging samples; its results are not an error-rate estimate for a company’s supplier documents or today’s chosen service. We use the task distinctions, not historical leaderboard scores, to frame the review.

Our editorial recommendation is to separate two acceptance questions: did the workflow recover the relevant values, and did it preserve what those values mean in this document? Both matter before generation turns a structured record into a persuasive narrative.

## Keep source evidence attached to important fields

For each field that could change the brief’s recommendation, retain the extracted value, its field name, the document version and a source location. A page number helps. Where available, a highlighted region or bounding polygon makes it easier to inspect the actual text and surrounding labels.

Do not treat a document-level citation as sufficient for every field. If a quote contains several subtotals, a link to the PDF leaves the reviewer searching for the relevant one. The review interface should expose the value and supporting region together, with enough surrounding context to identify the row or section.

In its [14 October 2026 practitioner account](https://devblogs.microsoft.com/foundry/content-understanding-confidence-grounding-hitl/), Microsoft describes combining source grounding with confidence-based review in Azure Content Understanding. It emphasizes calibration on representative documents. This is a vendor account, not independent verification of its customer outcomes. We do not reuse its reported review-time or accuracy improvements as expected results for another organization.

Grounding makes a claim inspectable; it does not make it correct. A highlighted region can support the characters while revealing that the extracted field name is wrong. Keep the original document available so a reviewer can inspect context beyond the crop.

## Check the relationships in a fictional supplier quote

Imagine an invented quote for ten standard equipment kits at a unit price of 12.50, with a line subtotal of 125.00. The quote separately lists a delivery charge and states whether that charge is included in a later total. These values illustrate checks; they are not actual prices or a purchasing recommendation.

The proposed review separates character recognition from interpretation:

| Extracted item | What to inspect on the page | Why a plausible value can still be wrong |
| --- | --- | --- |
| Quantity | Quantity column in the correct product row | A nearby pack-size number can be mistaken for the ordered quantity |
| Unit price | Price heading, currency and row alignment | A subtotal can be assigned to the unit-price field |
| Line subtotal | Same row and the quote’s stated pricing convention | Arithmetic may use the wrong quantity or ignore a stated adjustment |
| Delivery charge | Label and whether it is included in the total | Adding an already included charge counts it twice |
| Quote identifier | Exact characters and document version | Similar-looking letters and digits can point to a different record |

For the simple invented line, ten multiplied by 12.50 gives 125.00. That consistency check is useful, but it does not prove the extraction correct. Two misassigned fields can agree mathematically, or the document may contain discounts that require a different calculation. Define the applicable relationship before testing it.

Preserve the distinction between the document’s stated subtotal and a subtotal calculated by your workflow. If they disagree, expose both values and the discrepancy. Do not silently replace the source figure with a generated correction and then cite the document as though it supplied that correction.

Dates and selection marks deserve similar care. If a date is ambiguous under the document’s conventions, record the unresolved interpretation. If a form shows two option labels, establish which mark belongs to which label. Word presence alone cannot establish a selected answer.

## Use confidence to prioritize review, with its limits visible

[Microsoft’s Document Intelligence guidance](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/accuracy-confidence?view=doc-intel-4.0.0) describes confidence estimates for extraction results and notes that not all fields return scores. It also distinguishes confidence associated with recognized text from confidence associated with extracted fields. Those are product-specific signals; neither should be substituted for an assistant’s conversational claim that it is certain.

Document Intelligence and Content Understanding are distinct services. The cited blog’s description of confidence modeling in Content Understanding should not be assumed to describe every Document Intelligence model or another vendor’s extractor. Check the supported output for the actual service, model and API version used.

Before choosing an automatic-acceptance threshold, compare scores with checked answers on representative documents. Include the layouts, scans and field types the workflow will encounter. Inspect incorrect results that receive high scores as well as correct results sent unnecessarily for review. A threshold that works for one field and document family may be inappropriate for another.

Keep a missing confidence value explicit. It should not become high confidence because the field contains text, or zero because a numeric default was convenient. Decide what validation or review applies to that missing-signal case. This is a proposed acceptance policy, not a universal score cutoff.

## Evaluate extraction before evaluating the final brief

Prepare a small set of permitted sample documents with expected values and source locations checked in advance. Include rotated or low-quality scans, repeated headers, similar-looking identifiers, table rows spanning pages and fields absent from the source. Use only variations relevant to the intended workflow.

First compare extracted fields with those expected records. Then evaluate whether the brief preserves accepted values, separates calculated figures from quoted figures, and leaves unresolved fields unresolved. A field can be extracted correctly and still be misrepresented in the prose; these are separate failure stages.

Record omissions as well as incorrect values. A workflow that extracts a correct unit price but misses the delivery condition can produce a misleading comparison. Acceptance should reflect the fields required for this decision, rather than an impressive average across many easy fields.

When a check fails, route it to the appropriate remedy: obtain a clearer source, correct the extraction, resolve an ambiguous convention or revise the generated brief. Asking the writing model to try again against the same incorrect record may only produce a smoother version of the same mistake.

## Retain corrected values and the reason they changed

Save the original extraction alongside any reviewed correction, with the document version and review status. This lets the team distinguish an extractor error from a later document revision. If a supplier sends a revised quote, do not merge old and new values into one apparently consistent record.

Use recurring corrections to improve the workflow. Repeated row-assignment errors may require changes to parsing or schema design; missing evidence regions may require a different review interface. Track correction effort and the consequential fields that still fail, rather than counting only how many PDFs were processed.

Our [long-document coverage guide](https://skillsforbusiness.ai/blog/gemini-long-decode-token-budget-document-coverage/) addresses whether the model has enough of the right material to support a report. Field verification comes earlier: establish what the source actually says before treating the extracted record as trusted input. Start with one recurring document family and the few fields that could change the resulting business decision.

## Sources & further reading

Sources checked for this article. Research findings and vendor claims are distinguished in the text.

1.  [OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning](https://arxiv.org/html/2501.00321v2)Fu et al.; arXiv · Accessed 31 October 2026
2.  [Interpret and improve accuracy and confidence scores](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/accuracy-confidence?view=doc-intel-4.0.0)Microsoft Learn · Accessed 31 October 2026
3.  [From extraction to action: Using grounding and confidence to build reliable document workflows](https://devblogs.microsoft.com/foundry/content-understanding-confidence-grounding-hitl/)Microsoft Foundry Blog · Accessed 31 October 2026

Produced with AI-assisted research and writing. Examples and recommendations are our editorial synthesis unless attributed. This article does not claim independent product testing or human expert review. [Read our editorial policy.](https://skillsforbusiness.ai/blog/editorial/)

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