How Feedback-Based Reprocessing Improves AI Document Extraction with Docspire

AI-powered document processing has transformed how organizations handle invoices, bank statements, purchase orders, and other structured documents. Instead of manually extracting information, modern AI systems can automatically identify fields, extract values, and organize them into structured data.

However, real-world documents are rarely perfectly structured. Documents often contain custom layouts, non-standard field labels, complex tables, or inconsistent formatting. To understand what AI document understanding actually involves, it helps to see why even advanced AI extraction systems may occasionally misinterpret certain fields during the first pass.

This is where feedback-based document reprocessing in Docspire becomes incredibly valuable.

Feedback-based document reprocessing allows users to correct AI extraction results by giving simple natural language instructions, enabling the system to reprocess documents with improved accuracy and better understanding of document-specific formats.

Docspire’s feedback-driven reprocessing feature allows users to guide the AI using simple natural language instructions, helping the system understand how data should be extracted from specific document formats. Over time, this interaction enables Docspire to process similar documents with greater accuracy and consistency.

In this article, we’ll explore how Docspire’s feedback-based reprocessing works, when to use it, and how to write effective feedback to improve document extraction results.

What Is Feedback-Based Document Reprocessing in Docspire?

Feedback-based reprocessing in Docspire is a feature that allows users to improve document extraction results by providing instructions to the AI after the initial processing attempt.

If the system misidentifies fields, extracts incorrect values, or formats data improperly, users can provide feedback describing how the information should be extracted.

Docspire’s AI then reprocesses the document using the provided guidance, producing a revised extraction result.

Once feedback is provided, Docspire reprocesses the same document using updated instructions, adjusting field detection, formatting, and structure so that future extractions become more accurate and consistent.

Unlike traditional rule-based automation systems that require technical configurations or template creation, Docspire’s feedback-based processing works with simple natural language document instructions, making it accessible for both technical and non-technical users.

For example:

• “Invoice number is in the top-right corner.”
• “Total amount is the final value labeled ‘Amount Due’.”
• “Format dates as YYYY-MM-DD.”

These instructions help Docspire refine its understanding of the document layout and data structure, improving extraction results during reprocessing.

Why Feedback-Based Processing in Docspire Matters

Organizations often deal with documents that vary widely in format, even within the same document type. Understanding why extraction accuracy matters more than you think makes it clear how small errors in missed or incorrect fields can compound over time.

For example:

• Different vendors may format invoices differently
• Shipping documents may use unique column labels
• Financial documents may use non-standard date or currency formats
• Tables may be skewed or misaligned due to scanning or OCR artifacts

In these situations, traditional automation approaches require manual rule configuration or template setup, which can be time-consuming and difficult to maintain.

Docspire’s feedback-based reprocessing solves this challenge by allowing users to guide the system interactively without needing technical expertise.

Key benefits of using Docspire’s feedback-based reprocessing include:

• Faster correction of extraction errors
• Reduced need for manual data entry
• Better handling of unusual document layouts
• Continuous improvement for similar documents processed in Docspire

When Should You Use Feedback-Based Reprocessing in Docspire?

Docspire’s feedback-based reprocessing feature is most useful when the extracted results do not fully match your expectations.

Common situations include:

Incorrect Document Type Detection

Sometimes the system may classify a document incorrectly. For example, a document might be identified as an invoice when it is actually a purchase order. Correct classification ensures that Docspire applies the appropriate extraction logic for that document type.

Missing or Incorrect Fields

Certain fields may be missed entirely or incorrectly extracted due to layout complexity or unclear labels. For example:

• Vendor name extracted from the wrong section
• Invoice number misidentified
• Tax amount missing from the extraction

Providing feedback helps Docspire locate the correct field during reprocessing.

Formatting Issues

Extracted values may not follow the required format. Examples include:

• Dates appearing as 12/Jan/2025 instead of YYYY-MM-DD
• Currency values including symbols when only numeric values are required
• Decimal quantities when whole numbers are expected

Using feedback, you can instruct Docspire to return the data in the required format.

Confusing Similar Fields

Documents often contain fields with similar names, such as:

• Subtotal vs Total
• Gross Weight vs Net Weight
• Current Period vs Year-to-Date values

Without guidance, the AI may occasionally select the wrong field. Feedback helps Docspire distinguish between similar fields and extract the correct values.

Table Alignment Errors

In some cases, document scans or layout issues may cause tables to appear skewed. This can lead to extraction problems where values are placed under the wrong column headers. See how context-aware AI handles inconsistent document formats to understand the underlying mechanism behind these misalignments.

For example, if alignment is misinterpreted, the system might place quantity under price or vice versa. Feedback helps Docspire correctly interpret table structures and column relationships.

Extracting Additional Fields

You may also want to extract fields that were not included in the original extraction configuration. For example:

• Shipping weight
• Tax breakdown
• Payment terms

Providing feedback allows Docspire to capture these additional fields during document reprocessing.

How to Reprocess a Document in Docspire

Reprocessing a document in Docspire is simple and takes place directly within the Document Queue interface.

Follow these steps:

1. Navigate to Main → Document Queue from the left sidebar in Docspire.
2. Locate the document you want to reprocess in the Document Processing Queue.
3. Click the Reprocess button in the Actions column.

The reprocessing option becomes available for documents with the status:

• Success
• In Review

Clicking the button opens the Reprocess Document dialog, where you can modify document settings and provide feedback for Docspire to apply during reprocessing.

Understanding the Reprocess Document Dialog in Docspire

The dialog includes two primary components that help guide Docspire’s AI during the reprocessing step.

Document Type Selection

The Document Type dropdown in Docspire allows you to change the classification of the document if it was detected incorrectly.

Supported document types include:

• Bank Statement
• Credit Report
• ID
• Invoice
• Mortgage Application
• Pay Slip
• Purchase Order
• Receipt

If you’re unsure whether classification was the issue, you can leave this field empty and allow Docspire to auto-detect the document type again.

In many cases, selecting the “Other” category during reprocessing in Docspire can help evaluate whether extraction results improve.

Feedback Field

The Feedback field in Docspire allows you to enter natural language document instructions (up to 500 characters) describing how the system should extract or format the data.

This feedback acts as guidance for Docspire’s AI during the reprocessing step. Clear and concise instructions typically produce the best results.

Writing Effective Feedback in Docspire

Providing feedback in Docspire does not require technical knowledge. Simple instructions that describe where the data appears or how it should be formatted are usually sufficient.

Here are several useful patterns.

Pattern 1: Tell the System Where to Look

If a field was missed or extracted incorrectly, describe its location within the document.

Template: “[Field name] is in the [location].”

Examples:

• “Invoice number is in the top-right corner.”
• “Total amount is in the last row of the table.”
• “Vendor name appears in the header section.”

Pattern 2: Specify the Correct Column

If the system extracted values from the wrong column, clarify the correct one.

Template: “[Field] is under the ‘[Column Name]’ column.”

Examples:

• “Unit price is under the ‘Rate’ column.”
• “Weight is under the ‘Net Wt (KG)’ column.”
• “Unit of measure is in the ‘UOM’ column.”

Pattern 3: Define the Correct Format

If formatting is incorrect, specify the format you want.

Template: “Format [field] as [format].”

Examples:

• “Format dates as YYYY-MM-DD.”
• “Format amounts as numbers without currency symbols.”
• “Return percentages as numeric values.”

Pattern 4: Fix Data Quality Issues

Sometimes extracted values include unwanted characters or spacing.

Template: “[Action] from [field].”

Examples:

• “Remove extra spaces from all field values.”
• “Trim leading zeros from account numbers.”
• “Remove commas from numeric amounts.”

Pattern 5: Combine Instructions

You can also combine multiple instructions when necessary.

Example: “Quantity should be whole numbers. Price is under ‘Unit Cost’ column. Remove currency symbols from amounts.”

Real-World Feedback Examples in Docspire

Here are common document extraction problems and how feedback resolves them. Knowing what to do when a vendor changes their invoice format gives additional context on why these patterns appear so frequently in practice.

Incorrect Invoice Total

Problem: The system extracts the subtotal instead of the final payable amount.

Feedback: “Total is the final ‘Amount Due’ at the bottom, not the subtotal.”

Incorrect Weight Columns in Shipping Documents

Problem: Net and gross weight values are pulled from incorrect columns.

Feedback: “Net weight is under ‘N.W. (KG)’ column. Gross weight is under ‘G.W. (KG)’ column.”

Missing Tax Value on Receipts

Problem: The tax amount was not extracted.

Feedback: “Tax amount is on the line labeled ‘VAT’ or ‘Tax’ above the total.”

Vendor Identification Error

Problem: The system extracts the buyer instead of the vendor.

Feedback: “Vendor name is in the ‘From’ or ‘Sold By’ section, not the ‘Bill To’ section.”

Line Items Merged Together

Problem: Multiple table rows appear as a single item.

Feedback: “Each row in the items table is a separate line item. Extract them individually.”

Multi-Page Document Duplication

Problem: Totals from multiple pages are extracted repeatedly.

Feedback: “Use summary totals from the last page only. Ignore page subtotals.”

Troubleshooting Common Issues in Docspire

If reprocessing does not improve results in Docspire, consider these tips.

Issue	Solution
Reprocessing doesn’t improve results	Try being more specific about field locations and exact labels. Include the document section where the data appears.
Wrong document type detected repeatedly	Manually select the correct document type from the dropdown before reprocessing. Add feedback mentioning the document type.
Feedback character limit reached	Focus on the most critical corrections. Use abbreviated but clear instructions. Consider reprocessing multiple times for complex issues.
Dates still in wrong format	Specify both the source format and the desired format. Example: “Convert dates from MM/DD/YYYY to YYYY-MM-DD format.”
Numbers include unwanted characters	Explicitly state: “Format amounts as plain numbers without currency symbols, commas, or percentage signs.”

How Feedback-Based Processing Fits into the Docspire Workflow

Docspire’s feedback-based reprocessing works alongside several other intelligent document processing capabilities within the platform. To see how this compares to legacy approaches, read about how agentic AI outperforms traditional OCR in AP automation.

These capabilities include:

• Document Queue – Monitor and manage document processing activity.
• Document Tracking – View detailed processing history for each document.
• Human Review – Manually validate and correct extracted data.
• Validation Rules – Automatically verify extracted values against business requirements.

Together, these features create a complete document automation pipeline within Docspire, combining AI-powered extraction with human oversight when needed.

Final Thoughts

Automating AI document extraction is challenging because documents rarely follow a single standardized structure.

Docspire’s feedback-based reprocessing feature allows organizations to bridge this gap by giving users a simple way to guide AI toward more accurate interpretations of their documents.

Instead of building complex extraction rules, users can simply explain what the system should look for using natural language document instructions.

Over time, this approach enables faster document reprocessing workflows, fewer manual corrections, and more reliable document automation with Docspire.