Asking a model whether its own draft is accurate is not fact-checking. Extract the claims that matter, match each one to an appropriate source and remove or narrow anything the evidence cannot support. Start with statements that could affect a reader’s money, safety or trust.

An AI draft can remain fluent when the underlying information is incomplete, outdated or contradictory. Fluency is a presentation quality, not evidence.

Start by defining a material claim

A material claim could change a reader’s decision or understanding. Examples include:

  • a product price or plan limit;
  • a security, privacy or compliance statement;
  • a performance result;
  • a legal or medical assertion;
  • a quotation;
  • a claim that one product is better than another;
  • a statement about what a company “always” or “never” does.

Descriptive transitions do not need citations. Specific, consequential facts do.

Step 1: extract the claims

Read sentence by sentence and copy every material claim into a table. Do this before editing the prose, because polished wording can make a weak claim harder to notice.

Claim Best source Checked Decision
Plan includes feature X Official pricing or plan documentation Date Verify/qualify/remove
Tool is suitable for a small team BenPicks criteria and supporting facts Date Explain reasoning
Vendor meets standard Y Certification registry or official evidence Date Verify/qualify/remove

Step 2: use the right source hierarchy

Prefer the source closest to the fact:

  1. laws, standards, regulators and official public bodies;
  2. official product documentation, pricing, terms and release notes;
  3. original research papers or datasets;
  4. credible reporting that links to evidence;
  5. secondary summaries for context only.

A vendor is the primary source for what it advertises, but not automatically an independent source for how well the product performs. Phrase the distinction clearly: “The vendor documents…” is different from “Independent testing shows…”.

Step 3: open and inspect every citation

Do not accept a plausible URL or title. Confirm that the page exists and supports the exact sentence. Check whether the evidence is current, conditional or limited to a different plan, country or product version.

For PDFs, find the relevant section rather than relying on a search snippet. For pricing, look for billing period, taxes, usage caps and footnotes.

Step 4: check dates and scope

AI tools change quickly. Record when the source was checked and identify which facts are time-sensitive. A source can be authoritative and still be outdated.

Watch for scope errors:

  • enterprise feature presented as available to all plans;
  • US terms applied globally;
  • a beta feature described as generally available;
  • a model capability attributed to the entire product;
  • an old test applied to a new version.

Step 5: verify numbers independently

Recalculate percentages, monthly equivalents and cost comparisons. Make units and billing periods explicit. If a result depends on assumptions, show them.

Instead of “Tool A is 50% cheaper,” write the plan names, billing period and calculation—or avoid the percentage if the plans are not equivalent.

Step 6: challenge recommendations

A recommendation is an analysis built from facts. Ask:

  • Which reader is this for?
  • Which criteria produced the conclusion?
  • Are all compared products judged on the same evidence?
  • What evidence would change the recommendation?
  • Who should choose something else?

Remove superlatives such as “best” when the comparison set or criteria are unclear.

Step 7: look for invented experience

Delete any claim that the publication bought, tested or used a product unless records support it. Evidence could include invoices, dated notes, screenshots, inputs, outputs and a documented method.

Never allow a model to create a personal story, customer count, quotation or performance result for narrative effect.

Step 8: use a separate verification pass

A second AI model can help identify unsupported claims and contradictions, but it should receive both the draft and the research pack. Its role is to flag, not to invent repairs.

The final reviewer should resolve each flag as:

  • verified — the source supports the sentence;
  • qualified — the sentence needs narrower wording;
  • attributed — it is a vendor or third-party claim;
  • removed — adequate evidence is unavailable.

Step 9: check the page around the article

Accuracy includes metadata, tables, schema, captions and calls to action. A corrected body can still be undermined by an outdated title or a rating in structured data.

Check affiliate disclosures and ensure commercial links are clearly identified. Verify that the update date reflects a real review.

A compact pre-publication checklist

  • [ ] Every material claim appears in the evidence ledger.
  • [ ] Each citation opens and supports the exact claim.
  • [ ] Prices and product limits were checked on official pages.
  • [ ] Quotations match the original wording and speaker.
  • [ ] Numbers and comparisons were recalculated.
  • [ ] Vendor claims are attributed.
  • [ ] First-hand experience is documented or removed.
  • [ ] The recommendation names its reader and trade-offs.
  • [ ] Metadata, tables and structured data match the article.
  • [ ] A human approved the final version.

What a completed fact-check should leave behind

The result should be more than cleaner prose. It should leave a visible record of the claims checked, the sources used, the dates reviewed and the decisions to verify, qualify, attribute or remove. AI can help locate inconsistencies, but the publisher remains accountable for every claim left on the page.

Sources