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Check AI-written business copy claim by claim before anyone publishes it. Freeze the exact draft, separate factual statements from advice and marketing language, rank claims by potential harm and freshness, and verify each material statement against an authoritative source that covers the same wording, time period, market, and population. Record the evidence, reviewer, date, and decision. Use a qualified human for specialist or high-consequence claims, then inspect headlines, metadata, structured data, captions, and alt text as carefully as the body.
An AI detector or a confident-sounding explanation cannot establish that a business claim is true. NIST calls confidently presented false or inconsistent output confabulation, and warns that generated citations and reasoning can also be misleading. Google Search Central says: It is critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing.
What accuracy checking must catch
AI-generated business content can contain several different defects at once. A sentence may be factually false, technically true but outside the stated market, outdated by publication day, or internally inconsistent with another paragraph. A citation-shaped URL may not exist, or the linked source may discuss a similar subject without supporting the exact claim. NIST describes these confidently presented errors and contradictions in its Generative AI Profile, published July 26, 2024, in section 2.2 (official PDF).
Accuracy review therefore asks more than whether the prose sounds plausible. For every material claim, establish what is being asserted, what evidence would prove it, whether that evidence applies to the same scope, and who is accountable for the final wording.
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- Factual claims: dates, prices, product capabilities, market size, performance, customer outcomes, regulations, company leadership, and named people.
- Interpretive claims: comparisons, causal explanations, forecasts, and statements about what a trend means.
- Non-factual language: opinion, advice, positioning, and creative phrasing. These still need review for misleading implications, but they are not verified in the same way as a statistic or quotation.
There is no established universal percentage for the accuracy of AI-generated business copy, nor a published guarantee that a particular manual workflow eliminates errors. Treat the process below as a control system, not a promise of perfect output.
1. Freeze the draft before reviewing it
Save a dated, read-only copy of the exact version under review. Include the prompt or source material used to create it when your organization’s policy permits, but do not let later edits silently change the text that reviewers checked. Give the file a version identifier and keep the same identifier in the claim log.
Freezing the draft prevents a common failure: a reviewer validates one sentence, an editor changes the number or qualifier, and the published version is no longer the version that was checked. If the copy changes materially, create a new version and recheck affected claims.
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Read sentence by sentence and split compound statements into individual assertions. A sentence such as “The service launched in 2022, serves 40 countries, and reduces costs by 30%” contains at least three claims with different evidence requirements. Record each separately so one supported detail does not make the others appear verified.
Flag claims that deserve a check
- Numbers, percentages, rankings, ranges, dates, and growth rates.
- Quotes, testimonials, customer results, endorsements, and named people.
- Product specifications, compatibility statements, guarantees, and feature comparisons.
- Legal, regulatory, tax, health, safety, security, or financial assertions.
- Market descriptions, competitor claims, geographic coverage, and statements about causes or effects.
- Images, charts, tables, captions, and alt text that communicate factual information.
Mark advice, opinion, and creative copy separately. They may not require an external citation, but they can still create a false impression if a reasonable reader would interpret them as a factual promise.
3. Prioritize the review queue by risk
Do not spend the same amount of time on every sentence. NIST’s AI Risk Management Framework 1.0 says risk treatment depends on context and that greater potential harm calls for more urgent and thorough management. The framework page also notes that a revised version is in progress, so verify its current status before describing any edition as the latest (NIST AI RMF section 3; NIST publication list).
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| Review factor | High-priority signal | Practical action |
|---|---|---|
| Potential impact if wrong | Could affect customer safety, money, legal compliance, reputation, or a major business decision | Require a qualified reviewer and primary evidence before publication |
| Freshness | Price, product feature, policy, regulation, leadership, or performance figure can change quickly | Check close to publication and record the verification date |
| Source traceability | No original filing, dataset, policy, contract, study, or product documentation is available | Escalate, soften the wording, or remove the claim |
| Claim-to-evidence match | Source covers a similar topic but not the same market, period, population, or definition | Limit the sentence to the source’s actual scope |
| Expertise needed | Meaning depends on specialist legal, medical, financial, scientific, or technical judgment | Send to the appropriate subject-matter expert |
This is an editorial prioritization method informed by NIST’s context-dependent guidance, not a published universal scoring standard. A low-impact evergreen definition can wait while a high-impact claim about a current regulation receives immediate attention.
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Start with the strongest available source: an original filing, official dataset, policy, contract, research paper, product documentation, or named source. A secondary article can provide context, but it should not replace the underlying evidence when the underlying evidence is available.
Test the scope, not just the topic
For each source, check the publication date, applicable geography, definitions, population or product version, measurement method, and comparison period. A study of enterprise customers in one country does not automatically support a statement about all small businesses worldwide. A price shown for one edition or one date does not support a permanent global price claim.
Rewrite the draft to match the evidence’s strength. If the source reports an association, do not turn it into a cause-and-effect statement. If it reports a range, do not publish a single precise figure. If it covers a sample, identify the sample instead of implying a universal result.
Inspect AI-supplied references yourself
Open every generated link. Confirm that the page exists, identify its author or issuing organization and date, and locate the passage that supports the sentence. NIST specifically warns that citations, explanations, and reasoning can themselves be confabulated. Never accept a URL, quotation, statistic, or explanation merely because it looks scholarly.
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Google’s guidance for fact-check content emphasizes an identifiable claim, traceable methods, and citations to primary sources. Those principles are useful for ordinary editorial review even when you are not publishing a fact-check page (Google ClaimReview documentation).
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5. Recalculate numbers and compare quotations
Use the source inputs to reproduce arithmetic rather than copying an AI-generated result. Check units, currency, rounding, denominators, date ranges, geography, and whether a percentage is a percentage-point change or a relative change. If a chart uses a truncated axis or a selective baseline, explain that context or replace the chart.
For quotations, compare the wording word for word with the original recording or publication. Retain the speaker or organization, date, and surrounding context. Ellipses, translations, and edited excerpts can change meaning. If the source does not contain the quoted words, remove the quotation rather than attributing invented language.
Illustrative claim check
Suppose a draft says, “Our platform cuts processing time by 40%.” The reviewer should locate the underlying test, identify the products and users included, verify how processing time was measured, reproduce the calculation, and determine whether 40% is an average, a maximum, or a result from one trial. The publishable wording might need a population, date, method, and qualifier—or the claim may need to be removed if those details cannot be established.
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6. Check freshness and internal consistency
Revisit volatile facts near publication. Prices, product features, policies, regulations, company leadership, availability, and performance figures can change after an AI model’s training data or after the first draft was generated. Put an expiry or next-review date in the claim log for facts that are likely to move.
Search the entire draft for repeated values and names. Compare body copy with tables, captions, call-to-action text, footnotes, title elements, and metadata. A page that says a product supports five integrations in one place and eight in another is inaccurate even if one number is correct. Check that units, currencies, capitalization, and product editions remain consistent.
7. Escalate claims that need human expertise
Human review is not a ceremonial sign-off. NIST notes that intervention may be necessary when an AI system cannot detect or correct its own errors. Set approval rules based on likely impact: a communications editor may approve a low-risk style change, while a lawyer, accountant, security professional, clinician, or engineer should review claims in their domain.
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Use an explicit escalation trigger
- The claim could change a customer’s health, safety, legal position, finances, or security decisions.
- The source is ambiguous, contested, inaccessible, or materially weaker than the wording.
- The statement makes a promise, guarantee, certification, or regulatory interpretation.
- The claim concerns a named person, customer outcome, confidential contract, or unresolved incident.
- Reviewers disagree about the evidence or the appropriate qualification.
Document who resolved the issue and what authority they used. If no qualified reviewer or adequate evidence is available, narrow the claim or do not publish it.
8. Review every surface that can reach a reader
Fact-checking the article body is not enough. Google Search Central asks publishers to manually review AI-generated content and relevant metadata that may appear in Search, including title elements, meta descriptions, structured data, and image alt text (Google’s generative-AI content guidance).
Pre-publication surface checklist
- On-page headline, subheads, summaries, tables, charts, captions, and footnotes.
- HTML title element, meta description, canonical information, and social-preview text.
- Structured data values, including prices, ratings, dates, availability, and author fields.
- Image alt text and filenames when they make factual or descriptive claims.
- Buttons, forms, calls to action, email subject lines, and snippets generated from the page.
- Translated or syndicated versions that may have changed qualifiers or numbers.
Review the rendered page as a reader would see it. A correct body paragraph can be undermined by an overstated title tag or structured-data price.
9. Keep a claim log and correction trail
A claim log makes the decision reproducible and gives editors a clear route for later updates. Store the exact final wording, not a paraphrase. Link to the source and note the relevant page, section, table, or passage. Record the access date because a live page can change.
| Draft claim | Risk / freshness | Evidence and passage | Source date / geography | Reviewer | Decision | Follow-up |
|---|---|---|---|---|---|---|
| Exact sentence or atomic claim | High / medium / low; volatile or stable | Direct source, page or section | Publication date and applicable market | Name or role | Supported / corrected / qualified / removed | Open issue or correction note |
For a published correction, preserve the previous wording, explain what changed, identify the evidence, and date the correction. Google’s ClaimReview documentation calls for traceable methods and a correction policy or user error-reporting mechanism for fact-check content. ClaimReview eligibility is not guaranteed, and ordinary business pages are not required to implement that markup.
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Writing-quality scores, plagiarism checks, and AI-detection scores answer different questions from factual verification. They may indicate stylistic similarity, copied text, or a model’s likelihood of having produced a passage; none establishes that an individual business claim is true. A human-written sentence can be wrong, and an AI-written sentence can be correct.
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NIST’s AI RMF recommends realistic, representative test sets, documented measurement methods, and ongoing monitoring for deployed systems (NIST validity and reliability guidance). Apply that principle to any internal quality tool: define what it measures, test it on representative content, document limitations, and monitor failures. Do not convert a detector score into a publication decision without checking the underlying evidence.
11. Apply Google’s search guidance without overgeneralizing it
Google says generative models can produce inaccuracies and asks publishers to fact-check and review AI-generated content before publication. It also says automatically producing many pages without added user value may violate its scaled-content-abuse policy. These are Google Search policies and quality guidance, not a universal legal disclosure rule for every business document (Google AI-content guidance).
Make pages useful to people first: add original explanation, accurate context, and a clear reason the page exists. When it helps the audience, explain how content was created or reviewed. Do not imply that a disclosure substitutes for evidence, and do not imply that a search feature guarantees factual accuracy.
Google’s people-first documentation covers originality, accuracy, and the role of automation in content (Google Search Central guidance). Recheck live policy pages before publication because search documentation and framework status can change.
12. Turn the workflow into a repeatable team control
- Assign ownership: name the writer, fact-checker, subject-matter approver, and final publisher.
- Set risk tiers: define which claim types require an expert, a primary source, a second reviewer, or a publication hold.
- Use one claim-log template: keep evidence, dates, decisions, and follow-up in the same system for every project.
- Set freshness windows: require same-day or near-publication checks for volatile fields and scheduled rechecks for evergreen pages.
- Version everything: tie the approved log to a draft identifier and re-open review after material edits.
- Monitor after launch: provide a way to report errors, track corrections, and feed recurring failure patterns into prompts and editorial training.
This process separates generation from verification. AI can help produce a draft or suggest questions, but evidence, scope, accountability, and the final publication decision remain human responsibilities.
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