Document Quality Assessment: Brutal Truths Every Organization Ignores

Let’s get brutally honest: most organizations have no idea what’s actually lurking in their documents. Sure, the files might look polished, but document quality assessment isn’t about font choice or bullet points—it’s about what happens when unchecked errors, imprecise language, or outdated information metastasize beneath the surface. In 2025, with AI document review and large language models (LLMs) rewriting the rules, the stakes for document quality control have never been higher. Yet, somewhere between the boardroom’s grand digital transformation vision and the everyday grind of compliance, something gets lost. This piece rips the bandaid off the myths, exposes the silent failures, and delivers hard-hitting insights for anyone serious about document analysis best practices. If you think your process is “good enough,” prepare to be challenged. If you want to know how to assess document quality for real—without getting blindsided by fines, AI hallucinations, or credibility meltdowns—read on. Because in the era of instant decisions, a single overlooked detail can silently sabotage everything.

Why document quality assessment matters more than you think

The silent cost of bad documents

When organizations ignore robust document quality assessment, the fallout isn’t always immediate—but it’s inevitable. On the surface, a contract typo or a missing compliance reference might seem trivial. Underneath, though, poor document quality seeds chaos: unreliable business insights, flawed AI outcomes, regulatory headaches, and yes, real financial losses. According to the World Quality Report 2023–24, quality engineering is now a boardroom priority, largely because businesses can’t afford to let bad data and documents slip through the cracks. In fact, research from Atlan (2024) found that while 89% of organizations recognize data quality’s importance, only 22% actually have a dedicated data quality program. That gap isn’t just an oversight—it’s an unspoken liability.

Here’s the kicker: these silent failures are costly. Industry studies consistently tie poor documentation to regulatory fines, lost revenue, and operational breakdowns. Inaccurate medical records trigger lawsuits, flawed financial disclosures invite audits, and weak technical manuals can endanger lives. The cost isn’t always measured in dollars—sometimes it’s credibility, trust, or future opportunity.

Table 1: Major business impacts of bad document quality
Source: Original analysis based on World Quality Report 2023–24, Atlan 2024, Capgemini 2024

“Quality gaps in documentation aren’t just an IT problem—they’re an existential risk for the business. The organizations that get blindsided are the ones who think it’s ‘someone else’s job.’” — Senior Director, Quality Engineering, Capgemini (World Quality Report 2024)

How quality failures spark chaos across industries

If you think document quality assessment is a back-office chore, think again. When documentation fails, the dominoes fall across every sector—finance, healthcare, logistics, tech, you name it. Take healthcare: a single error in patient records can lead to misdiagnosis or denied insurance claims. In finance, inaccurate disclosures trigger investigations and tank shareholder trust. Logistics companies regularly lose millions due to documentation mismatches in supply chain tracking. These aren’t edge cases. They’re industry norms when quality assessment is an afterthought.

What drives these failures? Flawed assumptions, outdated templates, and unchecked human error. In the era of AI document review, the risks multiply. A poorly structured document can confuse even advanced algorithms, leading to false analytics or biased automation outputs. According to a recent NIAO quality report, “A single lapse can snowball, disrupting not just internal processes but also client relationships and market reputation.”

• Healthcare: Erroneous records can jeopardize patient safety, trigger lawsuits, and undermine trust in providers. According to the National Institutes of Health, documentation errors have been linked to major adverse events.
• Financial services: Missing disclosures or ambiguous contract language have led to multi-million-dollar settlements and regulatory crackdowns.
• Technology: Poorly documented APIs or technical manuals stall product rollouts, increase support costs, and frustrate users.
• Manufacturing & Logistics: Incomplete supply chain documents delay shipments, result in customs fines, and disrupt global operations.
• Legal & Compliance: Outdated clauses or vague definitions create loopholes, increasing litigation risk and non-compliance fines.

The consequences of poor documentation spiral fast. But here’s what stings: most of these disasters were preventable with rigorous, ongoing document quality assessment.

The myth of the 'good enough' document

Let’s call out the biggest lie in corporate documentation: “It’s good enough.” No, it isn’t. In high-stakes environments, the difference between “good enough” and “bulletproof” is the difference between business-as-usual and existential disaster. Quality assessment isn’t a checkbox—it’s an ongoing discipline. A “good enough” approach ignores context, audience, regulatory shifts, and the creeping entropy of outdated information.

A 2024 survey by TestResults found that most organizations only review documents prior to major audits or product launches. That’s reactive, not proactive. Worse, it creates a false sense of security. According to the Capgemini World Quality Report 2023–24, organizations with ongoing assessment cycles are 50% less likely to experience critical documentation failures.

“There’s no such thing as ‘good enough’ in compliance-driven industries. The standard changes, the risks evolve, and yesterday’s best practice is today’s blind spot.” — Quality Lead, Global Audit Firm, TestResults, 2024

The “good enough” myth endures because shortcuts are seductive. But in the real world, those shortcuts are where the cracks begin to show. If you’re not actively challenging your own document standards, you’re already behind—and your competition knows it.

Document quality assessment: foundations & frameworks

What defines document quality in 2025?

Ask 10 professionals to define document quality and you’ll get 15 answers. But in 2025, consensus is emerging around several non-negotiables: accuracy, clarity, compliance, and intent. These are no longer academic ideals—they’re operational imperatives. A quality document is one that communicates precisely, aligns with regulatory frameworks, and achieves its intended outcome without ambiguity or risk.

• Accuracy: Contains only verified, current information; all figures and references are up-to-date.
• Clarity: Language is clear, concise, and accessible to its intended audience; no jargon overkill.
• Compliance: Meets all relevant legal, regulatory, and industry-specific standards.
• Intent: Clearly serves its purpose, whether it’s to inform, persuade, contract, or instruct.

High-quality documents don’t just tick boxes; they reduce cognitive friction, prevent disputes, and fuel decision-making. According to the NIAO 2023–24 report, “Document quality is a moving target—it requires regular reassessment as standards, technologies, and expectations shift.”

In practice, document quality assessment involves more than a spellcheck or a quick skim. It’s a multi-layered process, demanding both human judgment and systematic review—often with AI-powered tools like those offered by textwall.ai/document-analysis.

Key pillars: accuracy, clarity, compliance, and intent

Every effective document quality assessment framework is grounded in a handful of foundational pillars. Here’s what separates truly robust assessment processes from superficial reviews:

• Accuracy: Regularly updated facts, double-checked calculations, and confirmation of cited sources.
• Clarity: Unambiguous language, logical flow, clear headings, and consistent use of terminology.
• Compliance: Active cross-referencing with current regulations, standards, and corporate policies.
• Intent: Purpose-driven structure; every section, paragraph, and table serves a strategic role.

Without these pillars, documents quickly devolve into liabilities—confusing, non-compliant, or outright misleading.

Table 2: The four pillars of document quality and their assessment methods
Source: Original analysis based on Capgemini 2024, NIAO 2023–24

The hidden layers: semantic, visual, and structural integrity

Beyond surface-level content, true document quality lies in the invisible architecture. Semantic integrity ensures that concepts are used consistently and that meaning doesn’t shift unpredictably—critical in technical and legal writing. Visual integrity is about layout, accessibility, and how information is visually prioritized—a messy doc isn’t just ugly, it’s functionally broken. Structural integrity, meanwhile, refers to logical sequencing, consistent formatting, and metadata accuracy—crucial for automated processing or AI document review.

Ignoring these layers is risky. For instance, semantic drift (where a term gradually takes on a new meaning) can nullify contracts or compliance statements. Broken formatting can corrupt AI extraction or even lead to lost data during migration. According to the World Quality Report, advanced analytics now flag these “invisible” issues more often than outright content errors.

The best document quality assessment processes blend human expertise with digital tools that can parse these hidden layers, audit for consistency, and surface issues before they blow up.

Manual vs. automated assessment: the real showdown

Why old-school methods still matter

It’s tempting to believe that AI and automation will sweep away the need for human reviewers. But here’s a counterpoint: manual document quality assessment, for all its slowness, still catches context-driven errors that machines routinely miss. Experienced reviewers spot tone mismatches, ambiguous phrasing, or cultural faux pas that automated systems overlook.

Manual reviews remain vital for documents involving negotiation, sensitive communications, or regulatory nuance. According to the Capgemini World Quality Report, hybrid teams—combining human and machine review—consistently outperform those relying on automation alone.

“Even the smartest algorithm can’t replace the human sense for subtext, implication, or intent. Manual review is where subtlety lives.” — Documentation Specialist, Capgemini World Quality Report 2024

1. Initial content review: Humans quickly spot glaring errors and context mismatches.
2. Compliance assessment: Regulatory nuances often require subjective judgment.
3. Stakeholder feedback: Real-world use cases reveal hidden gaps or ambiguities.
4. Continuous improvement: Feedback loops refine templates and best practices over time.

AI and LLMs: revolution or false promise?

AI document review and LLMs are transformative—but only when deployed thoughtfully. They excel at pattern recognition, rapidly scanning for inconsistencies, missing fields, or outdated references across massive datasets. For routine document assessment—think NDAs, invoices, or regulatory forms—automation is a game-changer.

But here’s the edge: AI is only as good as its training data and the clarity of the input. Garbage in, garbage out. Flawed source documents, ambiguous templates, or incomplete fields can lead to disastrous AI hallucinations or missed compliance triggers. In 2024, Capgemini found that 60% of enterprises experienced at least one major AI-driven documentation failure that required human intervention to resolve.

Table 3: Comparing manual, automated, and hybrid document assessment methods
Source: Original analysis based on Capgemini 2024, NIAO 2023–24

Ultimately, the revolution isn’t about replacing humans—it’s about amplifying their abilities and freeing them to focus on what automation can’t solve.

Hybrid approaches: finding the sweet spot

The real-world answer? Hybrid assessment. Organizations leading in document quality assessment don’t pit humans against machines—they orchestrate both. Here’s how:

• Use automated tools for initial triage: flagging missing sections, outdated references, or compliance lapses.
• Layer in human review for critical context, intent verification, and final approval.
• Continuously update assessment criteria as regulations, templates, and business needs evolve.
• Feed manual review feedback into AI models, creating a self-improving cycle.

In practice, this hybrid model reduces turnaround times, improves accuracy, and creates a living feedback loop. It’s not just about efficiency—it’s about resilience.

Field notes: real-world disasters and redemption stories

Case study: the million-dollar typo

Let’s get specific. In 2023, a multinational logistics company overlooked a minor typo in an international shipping contract—misplacing a decimal point in customs liability statements. The result? A legal dispute that tied up $4.2 million in inventory at the border for months, with cascading losses across Europe and Asia. The kicker: the typo had survived multiple draft cycles, slipping past automated spellchecks and hurried manual reviews.

How did it happen? Here’s the anatomy of the disaster:

1. Drafting: Original contract created using legacy template with outdated fields.
2. Review: Automated spellcheck missed context-specific numerical error.
3. Approval: Rushed manual review overlooked decimal placement.
4. Implementation: Customs flagged the issue, halting all shipments.
5. Resolution: Costly legal intervention and months of operational disruption.

The lesson: no system—manual or automated—is bulletproof in isolation. Only layered quality assessment caught at multiple stages can prevent million-dollar meltdowns.

When automation saves the day (and when it doesn’t)

Automation gets a bad rap when it fails, but let’s give credit where it’s due. In 2024, a healthcare provider implemented automated document review for patient intake forms. Overnight, error rates dropped by 70%, and reimbursement delays shrank dramatically. The system flagged missing insurance numbers and ambiguous physician notes, allowing staff to correct them in real time.

But automation isn’t always a savior. In another case, a finance company’s AI flagged “unusual” language in a legal disclosure—only to inaccurately redline critical, compliant clauses, creating a compliance risk of its own.

“Automation is a blunt instrument: invaluable for finding patterns at scale, but dangerous if you trust it blindly.” — Chief Compliance Officer, major healthcare provider (Capgemini, 2024)

The key is to let automation handle the repetitive, data-driven checks—and to empower human reviewers to apply context and judgment.

Inside textwall.ai: redefining document analysis

Amid this high-stakes landscape, tools like textwall.ai are pushing the frontier. Their AI-based document analysis platform leverages advanced LLMs to instantly summarize, categorize, and surface critical insights from even the densest reports. Rather than replacing human expertise, textwall.ai enables professionals—whether analysts, legal reviewers, or researchers—to focus on what machines can’t: nuanced decisions, strategic thinking, and context-rich assessment.

Key features of modern document analysis platforms:

• AI-driven summarization: Condenses lengthy documents into actionable insights.
• Automated compliance checks: Flags missing, outdated, or noncompliant sections.
• Customizable review workflows: Adapts assessment criteria to industry and use case.
• Continuous learning: Improves accuracy over time based on reviewer feedback.

By blending machine precision with human oversight, platforms like textwall.ai are raising the bar for document quality assessment—and helping organizations avoid the hidden pitfalls.

The anatomy of a flawless document: breaking it down

Step-by-step guide to mastering document quality assessment

1. Define the purpose: Understand the document’s intent, audience, and required outcomes.
2. Check for completeness: Ensure all required sections, fields, and attachments are present.
3. Verify accuracy: Cross-check all facts, figures, and references with trusted sources.
4. Assess clarity: Review for plain language, logical flow, and readability.
5. Validate compliance: Cross-reference document with relevant laws, regulations, and policies.
6. Audit structure and metadata: Confirm consistent formatting, correct headers/footers, and accurate metadata.
7. Run automated checks: Use AI or LLM tools to detect inconsistencies or missing content.
8. Solicit stakeholder review: Engage subject matter experts for feedback and validation.
9. Implement feedback: Refine content based on input, addressing any flagged issues.
10. Archive and document revisions: Track changes and version history for auditability.

This workflow isn’t just for compliance—it’s the backbone of document quality that stands up to scrutiny.

A flawless document isn’t a myth—it’s the result of relentless, multi-phase assessment using both human and machine intelligence.

Common traps and how to dodge them

• Assuming templates are infallible: Outdated forms often embed errors or omit new requirements.
• Over-relying on automation: AI flagging is helpful, but context matters—always combine with human review.
• Ignoring document metadata: Incorrect dates or missing version numbers can trigger compliance headaches.
• Skimping on stakeholder input: Documents meant for multiple audiences require cross-functional feedback.
• Failing to track revisions: Lack of version control invites confusion and duplication.

The most common trap? Treating document quality as a “one and done” event rather than a continuous process.

“Every time a document changes hands, there’s risk. The organizations that win are the ones who treat assessment as a living, breathing discipline.” — Documentation Process Consultant, NIAO Quality Report 2023–24

Dodging these traps demands vigilance, regular retraining, and the right mix of technology and expertise.

Self-assessment: is your document up to par?

Before you send that next report, ask yourself:

• Does the document have a clear purpose and audience?
• Are all facts, figures, and references validated?
• Is the language accessible and unambiguous?
• Have legal and compliance standards been met?
• Are structure, formatting, and metadata consistent?
• Was AI or automated review used, and were flagged issues addressed?
• Have key stakeholders reviewed and approved the content?

If you can’t answer “yes” to each question, it’s time for another round of quality assessment. A single oversight could spell disaster.

Controversies and debates: who sets the standard?

Quality by whose rules? The politics of assessment

Who gets to decide what “quality” means in a document? The truth is, standards are often set by whoever wields power—regulators, legal teams, or industry bodies. This creates tension between operational efficiency and compliance orthodoxy. Sometimes, document quality assessment devolves into a turf war: IT vs. Legal, Compliance vs. Operations, Management vs. Reality.

“Quality standards are always political. They reflect power dynamics as much as industry best practices.” — Documentation Policy Analyst, NIAO Quality Report 2023–24

The best organizations cut through the noise by making their assessment criteria transparent, collaborative, and adaptable to shifting requirements.

In practice, the politics of assessment can stall progress—but it can also drive innovation when stakeholders work together to redefine “quality” dynamically.

Regulations, compliance, and the moving target

Regulatory compliance isn’t static. Standards evolve, laws change, and yesterday’s compliant document is today’s liability. This is especially true in cross-border operations, where documents must satisfy multiple jurisdictions.

Table 4: Major regulations affecting document quality assessment
Source: Original analysis based on Capgemini 2024, NIAO 2023–24

The upshot? Quality assessment processes must be agile—regularly updated, thoroughly documented, and responsive to new regulatory landscapes.

Organizations that treat compliance as an ongoing, embedded discipline are best equipped to avoid disaster.

Ethical dilemmas in automated document assessment

Automation brings speed and consistency, but it also introduces ethical minefields. Key dilemmas include:

• Bias in training data: AI models trained on biased documents perpetuate those biases—sometimes invisibly.
• Privacy risks: Automated tools may process sensitive content without proper consent or controls.
• Accountability gaps: When automation flags—or misses—an error, who’s responsible?
• Transparency issues: Black-box algorithms make it hard to justify assessment outcomes to regulators or clients.

Automation must be governed by transparent audit trails, clear policies, and human oversight—not just for compliance, but for ethical integrity.

Organizations should view these dilemmas not as roadblocks, but as design challenges to create fair, transparent, and accountable document assessment processes.

Advanced strategies and future-proofing your process

Leveraging AI: beyond the basics

AI document review shouldn’t be a set-and-forget tool. The most advanced teams use AI for:

• Entity extraction: Surfacing key data points across thousands of documents.
• Semantic analysis: Detecting tone, sentiment, and intent drift.
• Anomaly detection: Flagging outliers or missing sections.
• Workflow automation: Triggering review cycles based on risk scores.

When layered thoughtfully, AI augments human judgment—surfacing issues that would otherwise remain invisible in massive document troves.

To truly future-proof, organizations must invest in ongoing training, custom model tuning, and regular audits of their AI tools’ performance.

Integrating feedback loops for continuous improvement

Quality assessment isn’t static. Organizations need built-in feedback cycles:

1. Collect reviewer feedback: After each assessment, gather insights on errors missed by automation.
2. Update templates and checklists: Regularly refine based on recurring issues or regulatory shifts.
3. Retrain AI models: Incorporate new data and flagged errors to improve performance.
4. Audit completed assessments: Periodically review past documents to identify emerging risks.
5. Share learnings: Disseminate findings across teams to prevent siloed mistakes.

A mature document quality process evolves—learning from mistakes, adapting to change, and driving organizational resilience.

Embedding feedback isn’t bureaucracy—it’s survival in a fast-moving regulatory and business landscape.

Scaling document quality assessment in enterprise environments

Enterprise-scale environments introduce unique challenges: volume, diversity, and velocity of documents. Scaling document assessment requires:

• Automated triage to prioritize high-risk documents.

• Role-based workflows, ensuring the right experts review the right content.

• Integration with document management and e-signature systems.

• Real-time analytics and dashboards for compliance tracking.

• Automated classification: Instantly sort documents by risk, type, or compliance requirement.

• Bulk review tools: Simultaneously assess and flag issues across thousands of files.

• Customizable checklists: Tailor assessment to department, region, or business unit.

• Robust version control: Safeguard against duplication, confusion, and lost changes.

The goal: consistent quality at scale, without sacrificing speed or nuance. As organizations grow, investment in scalable, flexible assessment tools is non-negotiable.

Beyond business: societal stakes and the future of quality

How document quality shapes trust and truth

Document quality isn’t just a business concern—it’s the bedrock of trust, truth, and informed discourse. Inaccurate or ambiguous documentation distorts decision-making, undermines public confidence, and—at societal scale—compromises democracy itself. According to the World Quality Report, as AI-driven decision-making proliferates, the need for trustworthy, transparent documentation is a global imperative.

In the age of disinformation, organizations that prioritize document quality aren’t just safeguarding their operations—they’re defending the integrity of public dialogue.

High-quality documentation allows stakeholders to hold one another to account, resolve disputes, and pursue common goals based on shared understanding.

Environmental and cultural impacts of document overload

The document deluge isn’t just a productivity problem—it’s an environmental and cultural challenge.

Table 5: Environmental and cultural impacts of document overload
Source: Original analysis based on Capgemini 2024, NIAO 2023–24

• Massive digital archives consume energy and complicate data governance.
• Printing unnecessary documents increases environmental risk and costs.
• Overwhelmed teams disengage, leading to higher error rates and staff churn.

Organizations that streamline, assess, and archive with intent can reduce both their environmental footprint and cultural stress.

What’s next? Predictions and provocations for 2030

Let’s ground this in reality: document quality assessment will remain a core business and societal function as long as people rely on written information. The tools will evolve; the stakes will only grow. By 2030, expect AI and human reviewers to work in seamless tandem—each catching what the other misses, each learning from the other’s strengths and blind spots.

The organizations that thrive will be those who see document quality not as a compliance burden, but as a source of strategic advantage, trust, and resilience.

Your action plan: mastering document quality assessment today

Priority checklist for flawless document assessment

1. Clarify document purpose and audience.
2. Ensure completeness, including all required sections and references.
3. Cross-verify facts, figures, and citations with trusted sources.
4. Assess for clarity and readability, using peer and AI review.
5. Validate compliance with current regulations and standards.
6. Audit structure, formatting, and metadata.
7. Run automated quality checks with tools like textwall.ai.
8. Gather and act on stakeholder feedback.
9. Document changes with robust version control.
10. Continuously update assessment practices as standards evolve.

This checklist is your frontline defense against document disaster. Treat it as a living tool, updated as your organization and its environment change.

Embed these steps in your daily workflow, and you’ll sleep better at night—and avoid costly surprises.

Quick reference: definitions, tools, and resources

Document Quality Assessment (DQA) : The systematic review of documents for accuracy, clarity, compliance, structure, and intent, using both human and automated methods.

AI Document Review : The application of artificial intelligence (LLMs, NLP, etc.) to analyze, summarize, and assess documents for errors, risks, and compliance.

Compliance Audit : A structured evaluation of documents against legal, regulatory, and policy requirements.

• World Quality Report 2023–24 – Annual industry benchmark for quality and compliance.
• NIAO Quality Report 2023–24 – Real-world lessons on documentation pitfalls and resilience.
• textwall.ai/document-analysis – Advanced tools for instant document analysis and review.
• Atlan Data Quality Study 2024 – Key statistics and trends in data/document quality.

Stay current. Bookmark these resources and revisit them regularly.

Key takeaways: what you can’t afford to ignore

• Weak document quality assessment is a silent, systemic risk—financial, reputational, operational.
• Automation is essential, but only when paired with critical human review and robust processes.
• Document quality is everyone’s problem—not just compliance or IT.
• The best teams treat assessment as a living practice, constantly adapting.
• Investing in document quality is investing in trust, truth, and business resilience.

“In an era of instant decisions, a single overlooked detail can silently sabotage everything.” — Industry Insight, World Quality Report 2023–24

Let this be your wake-up call: document quality assessment isn’t optional. It’s the backbone of your organization’s credibility, compliance, and competitive edge.

Supplementary explorations: adjacent topics & deeper dives

The evolution of document analysis: from papyrus to AI

Document analysis isn’t new—it’s ancient. Here’s how we got here:

1. Manual scribing (ancient times): Every document hand-written, every word scrutinized.
2. Printing press (15th century): Standardization and mass replication—quality still manual.
3. Typewriters and carbon copies (19th–20th centuries): Greater consistency, but prone to new types of error.
4. Word processors and spreadsheets (late 20th century): Digital editing, version control, and basic spellcheck.
5. AI and LLM-powered analysis (21st century): Real-time, scalable, intelligent review.

What’s old is new again: the fundamental need for accuracy, clarity, and intent persists, even as our tools evolve.

Misconceptions that sabotage your document strategy

• “Spellcheck is enough.” (Reality: It misses context, structure, and compliance.)
• “Templates guarantee compliance.” (Reality: Templates go out of date.)
• “Automation eliminates human error.” (Reality: It replaces one kind of error with another.)
• “Quality assessment is a one-time task.” (Reality: It’s a continuous, iterative discipline.)
• “It’s only for compliance.” (Reality: Poor quality cripples decision-making and trust.)

The greatest misconception? That document quality is someone else’s problem. In reality, it belongs to everyone.

“Complacency is the enemy of quality. The minute you stop questioning, you start falling behind.” — Industry Analyst, Atlan Data Quality Study 2024

Challenge every assumption. That’s how organizations stay ahead.

Document quality assessment across industries: what’s different, what’s universal

Some standards are universal—accuracy, clarity, intent. But every industry adds unique wrinkles:

Table 6: Document quality demands across sectors
Source: Original analysis based on NIAO 2023–24, Atlan 2024, Capgemini 2024

• High-volume industries (logistics, finance) need scalable automation.
• Regulated sectors (healthcare, legal) require meticulous compliance mapping.
• Academic and R&D environments focus on citation accuracy and reproducibility.

Despite these differences, the core principles—relentless assessment, robust process, and a blend of human and AI review—remain the same.

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Embrace document quality assessment as your competitive weapon. It’s not just about avoiding disaster—it’s about building trust, making better decisions, and outpacing the competition. The organizations who get this right are the ones who sleep soundly, knowing their documents won’t come back to haunt them.