How to Fact-Check an AI-Written Blog Post

To fact-check an AI-written blog post, extract its verifiable claims and compare them with original sources. A citation alone is not proof: check whether the source supports the actual numbers, conditions, and timing in the sentence. Asking another AI the same question can assist a review, but it does not replace checking the underlying evidence.

Start by extracting the claims that need checking

Prioritize statements whose errors could change a reader's actions, rather than reviewing every sentence with equal intensity. Prices, supported countries, plans, release dates, usage limits, policies, installation commands, and deletion procedures are common examples. Evaluations such as “fast” or “safe” also need a defined basis.

The sentence “Anyone can use this service for free” contains several claims. Does “free” cover only registration? Are there usage limits? Is access restricted by region or account type? Checking these conditions individually is more useful than searching the original sentence as a whole.

Google's guidance on generative AI content also discusses checking accuracy and reliability before publication. Review more than the body: look for exaggeration in titles, meta descriptions, and image alt text too.

Distinguish having a source from having supporting evidence

When opening a link, check the document's title and publisher first. Is it on the actual company's or institution's official domain? Is it an announcement or a usage guide? Does it describe the current version? Do not rely solely on a search snippet or the quotation supplied by an AI.

1. Find the claim's key terms and numbers in the source body.
2. Read the surrounding sentences to establish the audience, conditions, and exceptions.
3. Distinguish the announcement date from the date a change takes effect.
4. If the evidence matches, connect it to the claim. If it does not, revise or remove the statement.

Official sources serve different purposes. A launch announcement may describe a rollout plan, while a help article explains current eligibility. If they appear inconsistent, compare the target accounts, regions, and document update times before deciding that one is wrong.

Record the verdict and its reason

A table of claims and sources helps distinguish “I viewed the material” from “I verified the statement.” The following hypothetical examples illustrate a review method; they do not describe the actual terms of a particular service.

Simple verdicts such as “verified,” “verified with conditions,” and “unverified” are sufficient if you also record the reason. Before finishing, check that a conditional verdict has not remained an unconditional statement in the body.

Weakening the wording of an unverified claim does not create evidence. Remove it if it is unnecessary to the central conclusion. If it is essential, find better evidence or explain the unverified scope clearly.

Give AI a specific review task

Asking only “Is this article correct?” may produce a repetition of its original explanation. Narrow the assignment: first extract claims, then compare them with material you supply. If the AI cannot open links, explicitly tell it not to say that it did.

Extract claims in the draft below that require external verification.
Prioritize dates, prices, numbers, eligibility conditions, policies, and executable procedures.

Output: Original sentence / Specific claims to check / Required primary source / Missing conditions
Do not decide whether the claims are true or false yet.
Do not invent numbers or source URLs absent from the draft.

[Draft]
Paste the article to review here.

Next, provide evidence you have obtained and ask which parts support each sentence and which points the material does not establish. For long documents, request section references so you can locate the evidence again. The author still needs to open and verify those references.

Agreement between several AIs does not necessarily add independent evidence. They may share the same source or a widely repeated mistake. Use a second AI as an editor looking for omissions and contradictions, while checking the final verdict against sources.

Check numbers, procedures, and images separately

Amounts and percentages need units, periods, and baselines. Do not confuse a monthly subscription price with the monthly equivalent of an annual payment, or used capacity with remaining capacity. Recalculate from the original values and use consistent rounding.

Suppose a hypothetical price list says “$12 when billed monthly, or $120 when billed annually.” An AI summary of “available for $10 per month” has a correct calculation, 120 ÷ 12 = 10, but omits the payment condition. A clearer version is “An annual payment of $120 is equivalent to $10 per month; monthly billing costs $12 per month.” These are illustrative amounts, not actual service prices.

Where possible, verify commands and settings in a safe test environment. Do not run irreversible deletion, migration, or payment operations on a live system merely to check a tutorial. If execution is unavailable, distinguish what was verified from expected behavior and check documented menu labels and versions first.

Images require another distinction: a generated interface or chart must not be presented as a real product screen or experimental result. Compare educational labels and numbers with the text. For real screenshots, check whether readers using another version or account type can follow the same steps.

Carry the corrections through the title and body

After correcting a fact, ensure the title, excerpt, tables, and FAQ communicate the same conclusion. A body corrected to “selected plans” is not consistent with a title promising “free for everyone.” If AI rewrites the correction, check that it has not dropped the conditions again.

Present opinions as opinions and explain which facts support an inference. Topics such as law, health, and finance need closer attention to jurisdiction, timing, and appropriate expert review. Keeping the post as a draft is reasonable when an essential claim remains unverified.

For the search-policy context, see AI-generated content at scale and search engine policies. In everyday publishing, manage the question of AI use separately from verification of each article's claims.