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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFact-check AI-generated business content claim by claim before anyone publishes it. Freeze the exact draft, separate verifiable statements from opinions and advice, rank claims by potential harm and freshness, trace important claims to inspectable primary evidence, and record each decision. A fluent style or an AI-detector score is not proof that a statement is true.
For websites, review the words people read plus title tags, meta descriptions, structured data, image alt text, captions, charts and calls to action. Escalate legal, financial, health, safety and specialist claims to a qualified human reviewer.
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The core rule: verify claims, not fluency
Generative AI can produce polished prose while inventing facts, blending separate events, overstating a source or contradicting information in the prompt. NIST calls this confabulation: erroneous or false content presented confidently. Its Generative AI Profile (AI 600-1, July 26, 2024, §2.2) also warns that generated citations, explanations and chains of reasoning can be fabricated or misleading.
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That makes accuracy review an evidence exercise. A citation-shaped sentence is only a lead until you open the reference, identify the relevant passage and check that it supports the draft’s exact wording, time period, geography, population, product or legal scope. A writing-quality review, plagiarism check or AI-detector result may help with other editorial questions, but none establishes that an individual business claim is true.
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“It is critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing.”
Google Search Central, Google Search’s Guidance on Generative AI Content on Your Website
The workflow below is designed for a single article, campaign, web page or customer-facing document. Teams can turn it into a repeatable approval process without pretending that one score or one automated pass can guarantee accuracy.
Set a risk-based review queue
Do not spend the same amount of time on every sentence. Prioritize statements whose failure could cause greater harm, whose truth can change quickly, or whose meaning requires specialist judgment. This is a practical application of NIST’s context-dependent risk-management guidance, not a universal scoring standard.
| Review factor | High-priority signal | What to do |
|---|---|---|
| Potential impact if wrong | Could affect money, safety, legal rights, compliance, customer decisions or reputation | Require primary evidence and an appropriately qualified human approver |
| Freshness | Price, product feature, policy, regulation, leadership, performance result or other fact likely to change | Check the source close to publication and record the review date |
| Source traceability | No original filing, dataset, policy, contract, documentation or named study is available | Find an inspectable source, weaken the claim or remove it |
| Claim-to-evidence match | The source covers a similar topic but not the same market, period, population, definition or strength of wording | Rewrite to the supported scope or obtain better evidence |
| Expertise needed | Legal, regulatory, medical, engineering, security, financial or other specialist interpretation | Escalate to a subject-matter reviewer before approval |
A low-impact, stable description can usually receive a lighter check. A claim that could trigger a purchase, create a regulatory exposure or change a customer’s behavior deserves the most rigorous path even when it sounds obvious.
A 10-step accuracy workflow
1. Freeze the draft under review
Save a dated, identifiable copy before checking it. Include the draft version, author or system that produced it, intended audience, market and planned publication date. Reviewers should be able to point to the same wording later; otherwise a silent edit can make a source decision impossible to reconstruct.
Lock the text while fact-checking or use version control with a clear status such as “under review.” If a claim changes, preserve the previous version and reopen the relevant check rather than assuming the old approval still applies.
2. Extract atomic, checkable claims
Read once for meaning, then mark every sentence that asserts something about the world. Separate factual statements from recommendations, opinions, forecasts and creative language. Break compound sentences into individual claims: a sentence saying that a service is faster, cheaper and compliant contains at least three separate assertions.
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Flag names, dates, figures, quotations, customer outcomes, product specifications, market descriptions, rankings, comparisons and cause-and-effect statements. Also flag implied claims in headings, button text, image captions, charts, testimonials and footnotes. A short sentence such as “approved for use in Europe” still needs a defined approval, jurisdiction, product version and date.
3. Assign risk and freshness
Give each extracted claim a simple priority such as high, medium or low, and mark whether it is volatile. High-priority examples include financial performance, legal or regulatory statements, health or safety assertions, promises to customers and claims about named people. These examples are practical review cues, not an exhaustive list.
Use the five comparison axes in the table above. When several claims compete for limited review time, check high-impact and fast-changing claims first, then claims with weak traceability or a large gap between the evidence and the wording. Keep the rationale in the claim log so another editor can understand why a claim was escalated.
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Prefer the original filing, dataset, policy, contract, product documentation, technical standard, research paper or named organization that created the information. Secondary summaries can help you locate a source, but they should not replace it when the original is available.
At the source, check:
- Who published it and whether that author has the relevant authority.
- Publication, revision and effective dates.
- Geography, jurisdiction, currency and market covered.
- Definitions, population, sample, denominator and product or plan scope.
- Whether the evidence supports the draft’s exact certainty: “may,” “has been shown,” “will,” “the first,” and “guarantees” do not mean the same thing.
A source discussing the same general subject is not automatically evidence for the sentence in your draft. If the source supports only a narrower statement, narrow the copy and preserve the qualification.
5. Open and inspect every AI-supplied reference
Do not accept a generated URL, quotation, statistic or explanation because it looks plausible. Open the link, confirm that the page or document exists, identify the author and date, and locate the passage the draft relies on. Read enough surrounding context to catch exceptions, definitions and limitations.
For a quotation, compare the words character by character, confirm who said them, and retain the context that gives the sentence its meaning. If the reference is paywalled, unavailable, redirected to an unrelated page or missing the claimed passage, treat the claim as unverified until you find an accessible authoritative source.
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Reperform arithmetic from the source inputs rather than copying a percentage from generated text. Check units, currency, rounding, date ranges, comparison periods, denominators and whether a figure is a one-time allowance, a recurring amount, a laboratory result or a promotional value. A percentage can change meaning completely when the denominator changes.
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For charts and tables, verify every label, axis, legend, total and footnote. Confirm that the visual does not imply a causal relationship when the source shows only a correlation. Keep the original data or a stable source copy with the claim log so a later editor can reproduce the calculation.
7. Check freshness and internal consistency
Revisit volatile information close to publication. Prices, product features, policies, regulations, company leadership and performance figures can change after the initial draft. Record the date you checked each such item and state an “as of” date in the copy when that prevents ambiguity.
Search the entire draft for repeated facts and compare them across body text, headings, captions, tables, calls to action and metadata. Resolve conflicting numbers, product names, version labels, dates and geographic scopes. A page can be factually correct in one paragraph and misleading overall if another surface carries an older figure.
8. Escalate when consequences or expertise require it
Set approval rules before a deadline forces a rushed decision. A legal or compliance reviewer should assess regulatory claims; a qualified financial, medical, engineering, security or other specialist should assess claims in that domain. The reviewer does not need to rewrite the entire piece, but should see the exact claim, evidence, wording and unresolved questions.
NIST’s AI Risk Management Framework 1.0 guidance emphasizes context, representative evaluation, documented methods, ongoing monitoring and human intervention when an AI system cannot detect or correct an error. Its page notes that a revised framework is in progress, so check the current NIST status before describing any edition as the latest.
Escalation is especially important when a claim could cause physical harm, financial loss, discriminatory treatment, legal exposure or a misleading customer promise. If no qualified reviewer or adequate evidence is available, qualify or remove the claim rather than treating a confident model output as approval.
9. Review every publishing surface
Fact-check the material that can reach readers even when it is not in the main paragraph:
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- On-page headline and subheadings.
- HTML title element and meta description.
- Structured data, including fields that may appear as search features.
- Image alt text, captions, chart labels and transcripts.
- Author, date, price, availability and comparison boxes.
- Buttons, calls to action, testimonials, footnotes and downloadable files.
- Preview cards, snippets and translated or syndicated versions where your process controls them.
Google Search Central’s guidance on generative-AI content explicitly calls for manual review of AI-generated website content and relevant metadata that may appear in Search. It also says automated production should meet Search Essentials and spam policies, and that producing many pages without added user value may fall under its scaled-content-abuse policy. Those are Google Search rules, not a universal legal disclosure requirement for every business document.
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Google says publishers should add context about how content was created when that information is useful to the audience. Decide whether that context helps your readers understand authorship, review and accountability; do not present it as a blanket requirement for every page.
10. Close the loop with a claim log and correction trail
Record the final wording and the decision for each material claim. A useful log has these fields:
| Draft claim | Risk and freshness | Evidence and passage | Source date and geography | Reviewer | Decision | Follow-up |
|---|---|---|---|---|---|---|
| Exact sentence or atomic claim | High, medium or low; volatile or stable | Direct source, page, section or data row | Publication or effective date and applicable market | Name or role | Supported, corrected, qualified or removed | Open issue, recheck date or correction note |
Keep the log with the approved draft. If a published claim is later corrected, retain the original wording, correction date, reason, new evidence and approver. This creates an auditable trail and makes recurring errors visible across campaigns.
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How to rewrite unsupported claims
Reduce the scope before deleting useful information
When evidence supports only part of a sentence, preserve the supported portion. Replace an absolute promise with a measured description, identify the population or period, and name the source date. “The study reported a lower average in its tested group” is materially different from “the product always reduces costs.” Do not add a qualifier that still implies the unsupported conclusion.
Separate fact from interpretation
Label what the source reports and what your organization infers. A source may document an association; your draft may have turned it into a cause. A policy may describe eligibility; your draft may have turned it into a guarantee. Keep the observation, attribute the interpretation and remove certainty the evidence cannot bear.
Use an explicit unresolved status
If a claim is important but cannot yet be verified, mark it as open in the internal log and keep it out of the published copy. “Not checked” is not the same as “probably true.” A missing source, broken link or inaccessible dataset is a reason to pause, narrow or remove the statement.
Worked examples for business editors
The following are illustrative review patterns, not claims about any particular company or product.
Performance promise
Draft: “Our platform cuts support time by 40%.” Extract the figure, the verb “cuts,” the implied causal relationship and the scope “support time.” Ask for the underlying study or operational records. Check baseline, sample size, measurement definition, test period, comparison group, product version and geography. If the evidence covers one pilot, write that limited result and identify the period; do not generalize it to every customer.
Regulatory statement
Draft: “The service is compliant with Regulation X.” Identify the jurisdiction, applicable article or control, covered entity, assessment date and meaning of “compliant.” Obtain the governing text and any formal assessment, then send the wording to legal or compliance counsel. If the evidence supports only alignment with selected requirements, say so rather than implying a formal certification.
Market statistic
Draft: “Most businesses now use this approach.” Find the original survey or dataset. Verify who was surveyed, when, where, how “business” and “use” were defined, the response rate and the denominator. A sample of one sector or region cannot support a worldwide claim. If the source is old in a fast-moving market, add the fieldwork date or remove the statistic.
Customer quotation
Draft: “It doubled our revenue,” attributed to a customer. Confirm the customer’s identity and permission, compare the quotation with the original recording or written approval, and establish the comparison period and meaning of “revenue.” Do not let an AI system fill in a missing name, date or context.
Website-specific checks and Google guidance
Search visibility adds surfaces and policy considerations, but it does not change the underlying fact-checking rule. Google Search Central’s people-first content guidance emphasizes usefulness, originality and accuracy. Treat those as publishing-quality requirements, not as evidence that a generated claim is true.
If you publish an actual fact-check page, Google’s ClaimReview documentation describes requirements for eligibility for a fact-check rich result. It calls for an identifiable claim, traceable sources and methods, and a correction policy or user error-reporting mechanism. Eligibility is not guaranteed, and ClaimReview markup is not mandatory for every business page. Do not add the markup merely to make ordinary marketing copy look independently verified.
Before launch, inspect the rendered page and its source or CMS preview. Confirm that structured-data fields, title tags, descriptions and alt text match the approved wording, and that translations or templates have not reintroduced an obsolete claim. Keep a screenshot or export when the page is high risk so later changes can be compared.
Build an ongoing control, not a one-time ritual
Define roles and approval thresholds
Assign a content owner who gathers evidence, an editor who checks wording and scope, and a subject-matter approver for specialist claims. For high-impact content, require two-person approval or a documented exception. The exact threshold should reflect your organization’s likely harm, audience and legal obligations.
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Schedule a new review when a source changes, a product version or price changes, a regulation is amended, a named executive changes, a customer case is updated or a page is republished after a substantial edit. Put the next review date in the log rather than relying on memory.
Monitor after publication
Invite readers and staff to report factual errors, route reports to an owner and document the disposition. Correct the page, metadata and structured data together when needed. Preserve the correction trail and communicate material changes to affected customers or subscribers.
Evaluate the process itself
NIST’s risk-management guidance recommends realistic, representative test sets, documented measurement methods and ongoing monitoring for deployed systems. Apply that principle to your editorial operation: sample approved pages, test whether reviewers can reproduce decisions, inspect recurring failure modes and update prompts or templates that repeatedly create unsupported claims. Do not convert those process checks into a made-up accuracy percentage for all AI-generated business content; no directly applicable published statistic establishes a universal rate or a guaranteed reduction from this workflow.
Quick Recap
Final pre-publication checklist
- Save a dated version of the exact draft and identify its audience, market and publication date.
- Mark factual, numerical, quoted, comparative, causal, named-person and customer-outcome claims.
- Break compound sentences into atomic claims and separate facts from advice or opinion.
- Rank each claim by potential impact, freshness, traceability, evidence match and expertise required.
- Open every important reference and read the passage that supposedly supports the wording.
- Verify authorship, dates, jurisdiction, definitions, population, units, denominators and product scope.
- Recalculate arithmetic and compare quotations word for word with their original context.
- Rewrite, qualify or remove claims that the evidence does not support exactly.
- Obtain specialist approval for legal, financial, health, safety, security and other high-consequence claims.
- Check the headline, title tag, meta description, structured data, alt text, captions, charts, calls to action and snippets.
- Complete the claim log with source passage, review date, reviewer, decision and follow-up.
- Publish only after open high-risk items are resolved, then retain a correction and recheck trail.
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