How Do I Use AI for Document Analysis?

AI for document analysis is a workflow that turns PDFs and scans into (1) readable text, (2) structured fields/tables, and (3) trustworthy summaries or Q&A that can be traced back to the source.

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TL;DR

Use a pipeline: prepare documents → extract text and key fields with confidence → validate low-confidence results with humans → then summarize or answer questions only from verified text, showing citations and refusing when evidence is missing.

Run OCR on a pilot batch, then validate low-confidence fields first.

Define The Output And “Done” Criteria First

“Document analysis” can mean different outputs, pick the one you need before choosing tools:

• Searchable text (OCR only): make scans selectable and indexable.
• Structured extraction: capture fields and tables into a defined schema (CSV/JSON).
• Grounded summaries / Q&A: answer questions with citations to the exact page/section.
• Classification/routing: detect document type and send it to the right queue.

Success criteria:

• Which fields must be correct 100% of the time (e.g., payment totals)?
• What error rate is acceptable for low-risk fields (e.g., optional metadata)?
• What evidence must be stored (page number, bounding box, confidence, reviewer status)?

Use The Standard Workflow: Ingest → Extract → Validate → Summarize → Export

A reliable end-to-end flow looks like this:

1. Ingest & normalize: split files, standardize format, remove encryption.
2. Text extraction: OCR for images; text-layer extraction for born-digital PDFs.
3. Structure extraction: layout, tables, key-value pairs when positional context matters.
4. Field extraction: map results into a target schema with confidence and provenance.
5. Validation: human review for low-confidence/high-impact fields; sampling for the rest.
6. Summaries/Q&A: answer only from validated text, with citations.
7. Export: include audit columns so downstream systems can trust the data.

Step 1: Prepare Documents So OCR And Extraction Work Reliably

• Scan resolution (OCR): use ≥200 dpi; 300 dpi+ often yields better OCR, but test with your content and font sizes. (Google guidance: minimum 200 dpi; 300 dpi often best)
• Born-digital PDFs: if text is selectable, OCR may be unnecessary, but still validate layout and tables.
• Fix skew/orientation: rotation and skew errors are a common root cause of extraction failures.
• Split “mixed” PDFs: avoid multi-document files (invoice + statement) if you can; extraction performs better when a file is one doc type.
• Remove blockers: decrypt password-protected PDFs and reject corrupted pages early.

ASSUMPTION (practical starting point): start with a pilot batch that reflects real variance (vendors, templates, scan quality). Expand until new layouts stop changing your error rates.

Step 2: Extract Structured Data With Traceable Evidence

Define A Target Schema Before You Extract

Write down fields and formats up front (example):

• invoice_date (ISO date), vendor_name (string), total_amount (decimal), line_items[] (array)

Keep Provenance, Not Just Values

For each extracted field, store:

• doc_id, page number
• confidence score
• location evidence (e.g., bounding box or line reference, if available)
• raw text snippet that produced the value

This is what makes human review fast and makes audits possible.

Normalize Before Export

• Standardize dates, currency, units, and identifiers.
• Add deterministic checks (e.g., sum of line items ≈ total) to catch silent failures.

Step 3: Validate Accuracy With Human Review And Measurable Thresholds

Use Confidence Scores Carefully

Many extractors return a confidence score per field. AWS documents confidence scores as 0–100 and recommends using them alongside your use-case sensitivity.

Operational rule:

• High-impact fields (payments, compliance decisions) → human review unless confidence is consistently high and validated by a golden set.
• Lower-impact fields → sample review + monitor drift.

ASSUMPTION (common practice): maintain a labeled “golden set” you re-run after changes (new templates, model updates, preprocessing tweaks). Start small and grow it until it represents the variance you see in production.

Detect Drift Early

Track extraction performance by:

• vendor/template
• document source channel
• confidence distribution shifts
• error clusters (e.g., totals fail when stamps overlap table lines)

Step 4: Summarize And Answer Questions From Verified Text Only

If your job includes summaries/Q&A (“What does this contract say about termination?”):

• Retrieve then generate: fetch the relevant sections first, then answer only from those sections.
• Cite evidence: show page/section references so reviewers can verify quickly.
• Extract-then-summarize for key facts: if the question depends on a value (“What’s the total?”), extract and validate the value first, then summarize using the verified value.
• Fail safely: when evidence is missing or unclear, say what’s missing (page/section) instead of guessing.

Step 5: Manage Privacy, Compliance, And Prompt-Injection Risk

• Data minimization: store only what you need, and define retention for raw files and extracted outputs.
• GDPR scoping: if you process EU personal data, you may be subject to GDPR obligations; use the legal text as the reference point.
• HIPAA scoping: for covered entities/business associates, apply “minimum necessary” controls for protected health information (PHI) as described by HHS.
• Prompt injection (documents as untrusted input): OWASP lists prompt injection as a top LLM risk category; treat uploaded documents as potentially adversarial instructions and block unsafe actions.
• Risk governance: NIST AI RMF provides a general framework for managing AI risks in real deployments.

How To Do This With CustomGPT.ai

If your workflow includes interactive document Q&A and traceability:

1. Add reference documents to your agent’s knowledge base (policies, manuals, templates)
2. Enable Document Analyst so end users can upload files during chat
3. Configure upload limits and allowed file types per agent (size, word count, files per message)
4. Turn on citations so answers are traceable to sources
5. Set a conversation retention period aligned to your policy
6. Apply platform defenses and safe settings to reduce hallucinations and prompt-injection risk
7. For deeper usage patterns, follow Document Analyst best practices
8. For feature behavior, limits, and security notes, use the Document Analyst overview

Example: Turn Invoice PDFs Into Structured Rows

Goal: Convert a batch of vendor invoices into clean rows for downstream systems.

1. Prepare: ensure upright pages and adequate scan quality; split mixed PDFs into one invoice per file.
2. Extract: capture invoice_number, invoice_date, vendor_name, subtotal, tax, total_amount, and line_items[].
3. Validate: route low-confidence totals and line items to review; verify sum(line_items) ≈ total_amount.
4. Export with auditability: include:
   • doc_id, page, field_confidence, extracted_at
   • review_status, reviewed_by, reviewed_at
   • optional: source_hash (detects file changes)
5. Summarize exceptions from validated data:
   Generate a report like: “10 invoices missing totals; 6 have line-item sum mismatches,” based on verified extraction, not on raw guesses.

Common Mistakes

• Treating confidence as truth: confidence needs calibration against your golden set.
• Summarizing from raw OCR without retrieval/citations: increases hallucinations and misses critical clauses.
• Handwriting, stamps, and low-contrast scans: expect higher error rates; test separately.
• Multi-column layouts and rotated tables: layout detection matters more than OCR text accuracy alone.
• Template drift: a vendor redesign can quietly break extraction, monitor by source.

Conclusion

AI document analysis works as an ingest→extract→validate pipeline: keep provenance, review low-confidence fields, and generate summaries or Q&A only from verified text with citations.

Next Step: CustomGPT.ai’s Document Analyst supports this with a 7-day free trial.

FAQ

Do I Need OCR If My PDFs Already Have Selectable Text?

Not always. If the PDF is born-digital and text is selectable, you can often extract the text layer directly. OCR still helps when pages are images (scans), when text is embedded as raster content, or when you need consistent layout handling. Either way, validate tables/fields and store provenance so reviewers can verify outputs.

How Should I Pick A Confidence Threshold For Human Review?

Don’t pick one number globally. Start by labeling a representative set of documents and measure field-level accuracy at different thresholds. High-impact fields (payments, compliance decisions) should use stricter thresholds and more review than low-impact metadata. Confidence is model-specific; calibrate it to your observed error rates and monitor drift over time.

Can CustomGPT Analyze A Document I Upload During Chat?

Yes—when Document Analyst is enabled for an agent, end users can upload supported documents during chat and ask questions about them. The agent uses the uploaded content alongside the agent’s knowledge base and can provide responses with citations depending on configuration. Start with the overview and enablement steps.

How Do I Limit What Users Can Upload In CustomGPT Document Analyst?

Use per-agent Document Analyst settings to control allowed file types, maximum file size, word limits, and files per message. This is how you reduce risk and keep the analysis within predictable bounds for review and compliance.

What’s A Practical Way To Reduce Prompt Injection Risk From Uploaded Documents?

Treat documents as untrusted input: restrict what the system can do, require retrieval grounding, and avoid executing actions based on document instructions alone. In CustomGPT, keep safety-focused settings enabled (e.g., “My Data Only” patterns and anti-hallucination guidance) and follow the platform’s defense recommendations.