How to Analyze Long Documents With AI Without Missing Important Details

Analyzing a 150-page report with AI sounds straightforward: upload the document, ask for the key findings, and read the answer.

But a convincing answer does not necessarily mean the entire document was analyzed.

An AI system might emphasize the executive summary while overlooking a qualification in the methodology section. It might miss a critical table, combine information from different reporting periods, or produce a conclusion without a verifiable source reference.

These problems matter when you are reviewing contracts, research reports, policy documents, technical specifications, financial disclosures, or other lengthy materials.

To analyze long documents with AI reliably, you need more than a powerful model and a broad prompt. You need a structured workflow that tracks what was examined, what evidence supports each finding, and what still requires verification.

This guide explains how to build that workflow using document preparation, section mapping, evidence extraction, coverage checks, and source-based verification.

The goal is not to make AI sound more confident. It is to make your analysis more complete, traceable, and useful.

Why AI Can Miss Important Details in Long Documents

Long documents introduce problems that do not always appear in short files.

A ten-page document may be easy to inspect manually. A report containing hundreds of pages, appendices, charts, tables, and cross-references requires more deliberate organization.

1. Successful Upload Does Not Guarantee Complete Analysis

A tool may accept a file without making every part of that file equally accessible to the model.

Document-processing behavior can depend on the tool, plan, file format, text extraction method, and processing limits.

Even when a document fits within a supported upload limit, that does not guarantee that every paragraph, table, image, or appendix will be examined in the answer.

Treat upload completion as a technical step, not proof of analytical coverage.

2. Broad Prompts Encourage Selective Summaries

A prompt such as:

Summarize this 200-page report.

does not define which details must be preserved.

The resulting summary may focus on prominent themes while omitting exceptions, methodological limitations, minority findings, or important footnotes.

This is not necessarily a failure of the summary itself. The problem is using an open-ended summary as if it were a comprehensive review.

3. Important Information May Be Scattered

A document may introduce a claim in one section, qualify it in another, and provide supporting figures in an appendix.

If the analysis treats those passages independently, it may miss their relationship.

For example, a report might state that a project met its targets while later explaining that several performance measures were excluded from the assessment.

Both statements may be necessary to understand the result.

4. Tables and Figures May Be Misread

Long documents often contain information that cannot be understood from paragraphs alone.

Examples include:

• Financial tables.

• Process diagrams.

• Risk matrices.

• Technical charts.

• Statistical figures.

• Scanned appendices.

Some AI document workflows primarily use extracted text and may not fully interpret visual content.

For more on this problem, see Why ChatGPT Misreads PDFs and How to Fix It.

5. Source References May Be Incomplete or Incorrect

An AI-generated citation is not automatically reliable.

A cited page may contain related information without actually supporting the conclusion.

For important findings, you must check the original passage.

The key principle: a useful long-document analysis must distinguish what AI reported from what the source actually supports.

What Counts as Long Document Analysis?

Long-document analysis can involve several different tasks.

Understanding the distinction helps you choose the right workflow.

Task	Main Question	Typical Output
Summarization	What does the document say overall?	Summary
Question answering	What does it say about a specific issue?	Focused answer
Information extraction	Where are the required facts and values?	Structured data
Comparison	How do sections, versions, or claims differ?	Comparison matrix
Critical analysis	What conclusions, risks, assumptions, and limitations matter?	Analytical findings
Coverage audit	Which parts have been examined and verified?	Coverage record

These tasks overlap, but they are not interchangeable.

For example, extracting every deadline from a contract is different from summarizing the contract's commercial purpose.

Likewise, a research report may have a clear overall conclusion while containing important limitations that require separate analysis.

What Makes a Document “Long”?

There is no universal page-count threshold.

A 40-page technical document with dense tables may be harder to analyze than a 150-page report containing straightforward narrative text.

Complexity depends on factors such as:

• Total text volume.

• Number of sections.

• Document structure.

• Tables and figures.

• Cross-references.

• Language.

• Scan quality.

• Required analytical depth.

A better question is:

Can you verify that the analysis has addressed every relevant part of the document?

If the answer is unclear, a structured workflow is appropriate.

Prepare Your Document Before Uploading It

Good analysis begins with knowing what the document contains.

Check the File Format

Determine whether the file is:

• A text-based PDF.

• A scanned PDF.

• A Word document.

• A plain-text file.

• A mixture of text and image-based pages.

Text-based documents are generally easier to process.

Scanned documents may require OCR before their content can be reliably extracted.

If you need to extract editable text from a scanned document, follow How to Extract Text from a Scanned PDF With OCR and AI.

Check Document Completeness

Before analysis, confirm:

1. The expected page count.

2. Whether the table of contents matches the file.

3. Whether appendices are included.

4. Whether pages are missing or duplicated.

5. Whether the document has multiple versions.

6. Whether tables and figures are readable.

A missing appendix cannot be recovered through better prompting.

Preserve the Original

Keep an unchanged copy of the source document.

If you perform OCR, split the file, or convert it into another format, save the processed version separately.

For example:

long-document-project/
  originals/
    report_v1.pdf

  processed/
    report_v1_ocr.pdf
    report_v1_sections/

  analysis/
    structure_map.xlsx
    findings_matrix.xlsx
    coverage_audit.xlsx

This makes it easier to compare AI outputs against the original.

Consider Privacy and Permissions

Before uploading a document to an AI service, check whether you are authorized to process it there.

Contracts, personal records, internal reports, and confidential research may be subject to organizational policies or legal restrictions.

Use an approved processing environment, and avoid sharing sensitive information with a service unless its handling conditions are appropriate.

Choose an AI Tool for Long Document Analysis

Different tools support different workflows.

The best choice depends on the document, the questions, and the evidence requirements.

ChatGPT

ChatGPT can help organize document content, answer questions, extract information, and create structured analysis outputs.

It is useful for iterative workflows in which you define a task, inspect the answer, and request more focused analysis.

However, document-processing capabilities depend on the product, plan, and upload context.

In particular, support for embedded images and complex PDF visuals should not be assumed across every workflow.

NotebookLM

NotebookLM is designed around working with supplied sources.

It can be useful when you want to ask questions about source materials and review source-linked responses.

However, citations still need to be checked against the original content.

Dedicated PDF Tools

PDF-focused applications may offer advantages for document viewing, OCR, annotations, or direct inspection of source pages.

Some also provide AI-assisted document analysis.

Choose tools based on what they can actually process and how easily you can verify their outputs.

Tool Selection Checklist

Before selecting a tool, ask:

• Can it access the relevant text?

• Can it handle the document's size and format?

• Does it process tables and figures adequately?

• Can it identify the source of a finding?

• Can you inspect the original passage?

• Can you export results?

• Does its data handling meet your requirements?

A larger upload allowance does not necessarily mean more reliable analysis.

For a related comparison, see How to Summarize Multiple PDFs With AI Without Losing Important Details.

Step 1: Build a Document Structure Map

Before asking AI to analyze the document, identify its structure.

This is one of the most important steps in the workflow.

A structure map provides a reference against which you can later check analytical coverage.

Start With the Table of Contents

Record each major section and its page range.

For example:

Section ID	Section	Page Range	Content Type
S01	Executive Summary	1–8	Narrative
S02	Background	9–24	Narrative
S03	Methodology	25–46	Narrative and tables
S04	Findings	47–88	Narrative, figures, tables
S05	Risk Assessment	89–106	Risk matrix
S06	Recommendations	107–114	Narrative
S07	Appendices	115–120	Tables and supporting material

This is a fictional structure for demonstration.

If the document has no table of contents, identify major headings manually or use AI to propose a structure.

Prompt: Generate a Document Structure Map

Review the uploaded document and create a section inventory.

Include:

1. Section ID.

2. Section title.

3. Page range, where verifiable.

4. Main content type.

5. Important tables or figures.

6. Appendices or supplementary material.

Do not invent missing sections or page numbers.

If you cannot determine a page range, mark it as "Needs Verification."

Flag any pages or sections that appear unreadable or unavailable.

This is an inventory task, not a document summary.

Verify the Map

Compare the generated structure with the original document.

Check whether all major sections appear.

Pay particular attention to appendices and sections with unusual formatting.

Do not proceed as though the structure map is complete until obvious gaps have been resolved or recorded.

Step 2: Define What Must Be Checked

A comprehensive analysis still needs a clear purpose.

Without defined objectives, the analysis may produce many observations while missing the information you actually need.

Create an Analysis Question Set

Examples include:

• What are the document's main conclusions?

• Which findings are supported by quantitative evidence?

• What assumptions underlie the analysis?

• What limitations are acknowledged?

• Which risks are identified?

• What exceptions qualify the conclusions?

• Which dates, deadlines, or thresholds matter?

• What important information appears only in appendices?

Different document types require different questions.

A technical specification may require checking tolerances and dependencies.

A contract may require reviewing obligations, exclusions, and deadlines.

A research report may require examining methods, evidence, and limitations.

Define Critical Details

Create a list of details that should not be lost during synthesis.

Detail Type	Examples	Why It Matters
Numbers	Amounts, percentages, measurements	Small errors may change conclusions
Dates	Deadlines, reporting periods	Establishes timing
Exceptions	Exclusions, conditions	Limits the scope of a claim
Assumptions	Models, baseline conditions	Determines applicability
Risks	Uncertainties, failure points	Affects decisions
Sources	Pages, tables, appendices	Supports verification

This checklist becomes the standard for reviewing the final output.

Step 3: Analyze Each Section Systematically

Instead of relying on one broad prompt, analyze the document section by section.

This does not mean that every section needs equal attention.

It means each relevant section should have a recorded analytical status.

Use a Consistent Section Template

For each section, request:

1. Main purpose.

2. Important findings.

3. Supporting evidence.

4. Quantitative values.

5. Assumptions and limitations.

6. Exceptions or qualifications.

7. Unresolved questions.

8. Source references.

Prompt: Analyze One Section

Analyze Section [SECTION ID] of the document.

Focus only on the content of this section and any explicitly identified cross-references.

Return:

• Main purpose.

• Key findings.

• Important numbers, dates, and thresholds.

• Supporting evidence with source locations.

• Assumptions and limitations.

• Exceptions or qualifications.

• Unclear or unsupported claims.

• Questions requiring further review.

Do not infer facts that are not supported by the source.

Distinguish direct evidence from your interpretation.

If the section is incomplete or unavailable, state that clearly.

Preserve Cross-References

Some findings depend on other sections.

For example, a conclusion in Section S04 may rely on definitions introduced in S03.

Record those relationships.

A cross-reference field might contain:

S04 finding depends on methodology in S03 and Table A2 in S07.

This prevents the final report from presenting an isolated claim without its conditions.

Do Not Assume Every Section Was Processed

After analyzing each section, record its status.

Recommended values include:

• Not Started.

• In Progress.

• Analyzed.

• Needs Review.

• Verified.

• Unavailable.

The distinction between Analyzed and Verified is essential.

Analyzed means an output has been produced.

Verified means the relevant output has been checked against the source according to the defined review criteria.

Step 4: Create an Evidence and Findings Matrix

A findings matrix connects conclusions to their supporting evidence.

Without it, a long-document analysis can become a collection of plausible statements that are difficult to audit.

Recommended Matrix

Finding ID	Section	Finding	Source Location	Evidence Status
F001	S03	Method uses a defined sample	Page 31	Needs Review
F002	S04	Report identifies a performance improvement	Table 4	Needs Review
F003	S05	Risk depends on an external assumption	Page 94	Needs Review
F004	S07	Appendix qualifies a headline result	Appendix B	Needs Review

All entries are fictional examples.

Separate Evidence From Interpretation

For each important finding, maintain two fields:

Source Evidence: What the document actually states.

Analytical Interpretation: What the evidence may imply.

For example:

Source evidence:

The report states that the target was met under the revised measurement framework.

Interpretation:

The reported success may not be directly comparable with results from the earlier framework.

The interpretation may be reasonable, but it is not the same as the source statement.

Prompt: Extract Evidence for a Finding

For the following finding, identify the supporting passages in the document:

[FINDING]

Return:

• Exact supporting passage or a clearly labeled paraphrase.

• Document section.

• Page or source location, if verifiable.

• Whether the evidence directly supports the finding.

• Any limitations or contradictory evidence.

If no supporting evidence is available, label the finding "Unsupported."

Do not invent citations.

Verify Important Claims

Open the original document and inspect the cited passage.

Check whether:

• The passage exists.

• The page reference is correct.

• The wording has been represented accurately.

• The conclusion is supported.

• Nearby qualifications change the meaning.

For multi-document analysis, see How to Ask Questions Across Multiple PDFs With AI Without Mixing Up Sources.

Step 5: Run a Coverage Audit

A coverage audit is the main difference between a structured long-document analysis and an ordinary AI summary.

It asks whether the planned review has actually addressed the relevant document sections.

Create a Coverage Table

Section	Analysis Status	Evidence Reviewed	Open Issues
S01	Verified	Yes	None
S02	Verified	Yes	None
S03	Needs Review	Partial	Sampling definition
S04	Analyzed	No	Table values
S05	Verified	Yes	None
S06	Analyzed	No	Recommendation dependencies
S07	Not Started	No	Appendix review

This is a fictional example.

Calculate Administrative Coverage

You may track:

Analysis Coverage = Sections Marked Analyzed or Verified ÷ Total Planned Sections × 100

For example, if six of seven sections have reached Analyzed or Verified status:

6 ÷ 7 × 100 = approximately 85.7%.

This means six sections have reached the specified workflow status.

It does not mean AI understood 85.7% of the document or captured 85.7% of its important information.

Track Verification Separately

Verification Coverage = Sections Marked Verified ÷ Total Planned Sections × 100

If three of seven sections are Verified:

3 ÷ 7 × 100 = approximately 42.9%.

This administrative metric tells you how much of the planned review has reached the verification stage.

It is not a measure of factual accuracy.

Look for Hidden Gaps

A section may be marked Analyzed even when some of its content was inaccessible.

Therefore, coverage review should also ask:

• Were all expected pages available?

• Were relevant tables reviewed?

• Were figures interpreted or explicitly excluded?

• Were footnotes and appendices checked?

• Were cross-references followed?

• Were unresolved passages recorded?

Prompt: Audit Coverage

Compare the document structure map with the completed section analyses.

Identify:

• Sections with no analysis.

• Sections with incomplete analysis.

• Important tables or figures not reviewed.

• Appendices not examined.

• Missing source references.

• Findings awaiting verification.

• Cross-references that have not been followed.

Return a coverage audit table.

Do not mark any section as Verified unless its evidence has been checked against the original source.

Do not claim the document has been fully reviewed if any relevant content remains unavailable or unresolved.

Download the Long Document Analysis & Coverage Tracker

Analyzing a long document is easier when you can see which sections have been reviewed, which findings have supporting evidence, and which questions remain unresolved.

Download the Hozaki Long Document Analysis & Coverage Tracker, a free, editable Excel workbook designed to support systematic AI-assisted document reviews.

The workbook includes seven worksheets for documenting source files, mapping sections, tracking important findings, verifying critical details, recording unresolved issues, and reusing structured AI prompts.

It also calculates administrative analysis and verification coverage based on your section statuses.

Download the Excel tracker: Add the verified Hozaki-hosted download link here before publishing.

Coverage percentages represent workflow progress, not AI accuracy. All prefilled examples are fictional and should be replaced with your own review data.

Step 6: Synthesize Findings Without Losing Qualifications

After analyzing sections and reviewing coverage, you can prepare a consolidated report.

The challenge is preserving important details while reducing repetition.

Recommended Final Report Structure

1. Document identification and scope.

2. Main findings.

3. Supporting evidence.

4. Important numbers and dates.

5. Assumptions and limitations.

6. Risks and exceptions.

7. Contradictory or unresolved information.

8. Coverage and verification status.

9. Recommended follow-up questions.

Keep Limitations Close to Conclusions

Suppose a report states:

The pilot achieved its target under the revised evaluation criteria.

An oversimplified summary might say:

The pilot achieved its target.

The shorter statement omits a qualification that may matter.

A better synthesis preserves the condition:

The pilot met its target under revised evaluation criteria; comparisons with earlier results require checking whether the measurement methods are equivalent.

Prompt: Produce a Verified Synthesis

Prepare a consolidated analysis using the completed section findings and coverage audit.

Include only findings supported by the reviewed evidence.

Preserve important assumptions, limitations, exceptions, and reporting periods.

Distinguish:

• Verified findings.

• Findings awaiting review.

• Unsupported claims.

• Unresolved conflicts.

Include source references for important statements.

Identify sections or materials that were not fully reviewed.

Do not present incomplete coverage as comprehensive analysis.

The final report should be useful to someone who did not participate in the original review.

That requires transparency about what was checked and what remains uncertain.

Reusable Prompts for Long Document Analysis

The following prompts can be adapted for reports, contracts, research documents, and technical materials.

Prompt 1: Document Inventory

Create a document inventory before analysis.

Identify the title, version, date, page count if verifiable, major sections, appendices, tables, figures, and any unreadable material.

Do not summarize the document yet.

Flag missing or uncertain information.

Prompt 2: Critical Detail Extraction

Extract all material numbers, dates, thresholds, exceptions, and stated limitations relevant to [ANALYSIS OBJECTIVE].

Preserve the original units and reporting periods.

Include source locations where available.

Mark uncertain values for manual verification.

Prompt 3: Risk and Assumption Review

Identify the assumptions, dependencies, uncertainties, and risks discussed in the document.

Distinguish explicitly stated risks from your own analytical inferences.

Cite the relevant sections.

Do not invent risks that are not supported by the document.

Prompt 4: Contradiction Check

Review the completed section findings for potentially conflicting statements.

For each conflict, identify the relevant sections and evidence.

Check whether differences may be explained by reporting periods, definitions, scope, or assumptions.

Do not resolve a conflict without supporting evidence.

Prompt 5: Missing-Information Review

Based on the document structure and analysis objectives, identify questions that remain unanswered.

Distinguish between:

• Information absent from the document.

• Information not yet examined.

• Information unavailable due to processing limitations.

• Information that is ambiguous or contradictory.

Do not treat failure to find information as proof that it does not exist.

Prompt 6: Final Quality Check

Review the draft analysis against the evidence matrix and coverage audit.

Flag:

• Claims without source support.

• Missing qualifications.

• Incorrect or unverified numbers.

• Unresolved contradictions.

• Sections not reviewed.

• Conclusions stronger than the underlying evidence.

Return a correction checklist rather than rewriting unsupported claims as facts.

Common Long Document Analysis Mistakes

Mistake 1: Asking for One Summary and Assuming It Is Complete

A general summary can be useful for orientation.

It should not replace a structured review when omissions matter.

Better approach: Build a section inventory and analyze relevant sections systematically.

Mistake 2: Confusing Upload Limits With Analytical Completeness

A tool may accept a large file while still having processing limitations.

Better approach: Verify access to the relevant content and track what was actually reviewed.

Mistake 3: Ignoring Appendices

Important evidence, definitions, and limitations may appear outside the main narrative.

Better approach: Include appendices in the structure map and coverage audit.

Mistake 4: Treating AI Citations as Verified

A citation can point to the wrong page or fail to support the claim.

Better approach: Inspect the original passage.

Mistake 5: Losing Reporting Periods

A percentage from one year may not be comparable with a percentage from another.

Better approach: Preserve dates, definitions, and units in the evidence matrix.

Mistake 6: Mixing Source Evidence With AI Interpretation

A plausible interpretation may be presented as if it were directly stated in the document.

Better approach: Use separate evidence and interpretation fields.

Mistake 7: Overlooking Visual Content

Charts, scanned pages, and complex tables may not be fully accessible through text extraction.

Better approach: Inspect these elements separately or use an appropriate processing method.

Mistake 8: Reporting Coverage as Accuracy

A completed workflow checklist does not prove the analysis is correct.

Better approach: Track administrative coverage and source verification separately.

Practical Example: Analyzing a 120-Page Report

Consider a fictional 120-page organizational performance report.

The document contains:

• An executive summary.

• Background information.

• A methodology section.

• Performance findings.

• A risk assessment.

• Recommendations.

• Supporting appendices.

The objective is to identify the main findings, risks, assumptions, and unresolved issues.

This example is illustrative. It does not describe a real report or a tested AI result.

Stage 1: Build the Structure Map

The seven sections are recorded with their page ranges.

The appendix is included as a separate section rather than treated as optional material.

Stage 2: Define Review Questions

The analyst identifies five priorities:

1. Which targets were achieved?

2. Which measures were used?

3. What limitations affect the findings?

4. Which risks remain unresolved?

5. What evidence supports the recommendations?

Stage 3: Analyze Sections

Each section receives a structured analysis.

The methodology section identifies how performance was measured.

The findings section identifies reported results.

The risk section identifies uncertainties.

Stage 4: Record Evidence

A fictional findings matrix might contain:

Finding	Source	Initial Status
Target achievement depends on revised criteria	S03 and S04	Needs Review
Some outcomes rely on estimated data	S04	Needs Review
A risk remains unresolved	S05	Needs Review
Appendix provides additional measurement definitions	S07	Not Reviewed

These are illustrative entries, not verified facts.

Stage 5: Run the Coverage Audit

Suppose six sections have been analyzed, but the appendix remains unreviewed.

The administrative analysis coverage is approximately 85.7%.

The review cannot be described as complete because the appendix may contain information needed to interpret the main findings.

Stage 6: Resolve the Gap

The analyst reviews the appendix and checks whether its definitions change the interpretation of reported results.

Any newly identified qualifications are added to the findings matrix.

Stage 7: Produce the Final Report

The final analysis separates:

• Verified findings.

• Findings awaiting verification.

• Important limitations.

• Unresolved questions.

• Coverage status.

This is more useful than a polished summary that conceals uncertainty.

Frequently Asked Questions

Can ChatGPT analyze a 100-page PDF?

ChatGPT supports document uploads, but the ability to analyze a particular PDF depends on factors including file size, content, format, product capabilities, and processing limits.

A successful upload does not guarantee complete analysis of every page or visual element.

For important tasks, use section mapping and verification.

What is the best AI for analyzing long documents?

The best tool depends on your task.

ChatGPT may be useful for iterative analysis and structured outputs. NotebookLM may be useful for source-grounded research workflows. PDF-focused tools may provide more direct document inspection.

Compare tools using the actual document type and verification requirements.

Should I split a long PDF before uploading it?

Splitting can help organize processing or work around certain limitations.

However, it can also separate related evidence and remove context.

If you split a document, preserve section identifiers, page references, and the original complete file.

How do I stop AI from missing important details?

No prompt can guarantee that every important detail will be captured.

You can reduce omission risk by defining review objectives, mapping sections, analyzing systematically, tracking evidence, and auditing coverage.

Can AI analyze tables and charts in long PDFs?

Some tools and workflows can interpret visual elements, while others primarily use extracted text.

Capabilities depend on the product and processing context.

Verify important table values and chart interpretations against the original document.

How can I verify AI-generated document analysis?

Check important claims against their cited passages.

Confirm that numbers, dates, assumptions, and qualifications are represented accurately.

Maintain a findings matrix and record unresolved issues.

What if AI cannot find information in a long document?

Do not immediately conclude that the information is absent.

It may be in an unprocessed section, scanned page, appendix, or table.

Check the structure map and original document before recording the information as unavailable.

Is a coverage percentage the same as analysis accuracy?

No.

A coverage percentage based on completed sections is an administrative progress measure.

It does not prove that the analysis is factually accurate or that every important detail has been captured.

Can I use AI to analyze confidential documents?

Only when the processing environment and data-handling conditions meet your authorization, privacy, and organizational requirements.

For sensitive materials, consider approved or local workflows.

Final Checklist: Before Trusting an AI Analysis of a Long Document

Before relying on the final analysis, confirm that:

• The original document has been preserved.

• The correct document version was used.

• All expected pages and appendices are accounted for.

• Scanned or inaccessible content has been identified.

• The analysis objectives are clearly defined.

• A section structure map has been created.

• Relevant sections have been analyzed.

• Important tables and figures have been reviewed.

• Key numbers, dates, and units have been checked.

• Important findings have source references.

• Citations have been verified against the original.

• Evidence is separated from interpretation.

• Assumptions and limitations remain visible.

• Contradictions and unresolved issues are documented.

• Administrative coverage is not presented as factual accuracy.

• The final report identifies material gaps in the review.

Conclusion

AI can make long-document analysis faster and more manageable, but a fluent summary is not proof of comprehensive understanding.

The most reliable approach is to combine AI-assisted processing with a structured review workflow.

Start by checking the document's completeness and building a section map. Define the questions that matter, analyze relevant sections systematically, record supporting evidence, and perform a coverage audit before preparing the final synthesis.

Keep important qualifications, reporting periods, and unresolved issues visible throughout the process.

The objective is not to produce the shortest possible summary.

It is to produce an analysis that helps readers understand what the document says, what supports the findings, what has been verified, and what still needs attention.

For a related workflow involving several documents rather than one long report, read How to Ask Questions Across Multiple PDFs With AI Without Mixing Up Sources.