How to Fact-Check AI Content with an Evidence Ledger

The simplest way to fact-check AI content is to keep an evidence ledger: a small table that links each important claim to the material that supports it. It solves a common editing problem. A draft contains plausible facts, but nobody can explain where they came from or whether the source actually says the same thing.

This guide shows what to record, how to separate facts from inferences, how to handle sources that disagree and how to keep the ledger small enough that you will actually maintain it.

Record claims before polishing sentences

Give each decision-relevant claim an identifier. Then store the claim in plain language, the source URL, a short location reference, the date checked and the limits of the evidence.

Those limits matter. "Vendor documentation lists CSV export" is a different claim from "CSV export works on every plan." However, do not fill the ledger with every transition or opinion. Focus on statements a reader might rely on, such as feature availability, study results, eligibility rules and technical behavior.

Separate three kinds of statement

When you fact-check AI content, treat every claim as a documented fact, an observation or an inference. Each type needs different evidence and different wording.

This distinction stops a sourced fact from quietly growing into a broader recommendation. For example, a tool's supported export format does not prove that format meets a particular team's reporting needs.

Worked example: comparing subscription features

Imagine a hypothetical comparison of three scheduling products. One product page says "team sharing," while an older help article limits sharing to paid accounts. The ledger should keep that conflict visible rather than letting AI pick the more convenient answer.

So mark the claim as unresolved, check the current pricing and help pages, and ask the vendor if necessary. Until it is resolved, omit the plan-specific statement or explain the ambiguity. Also keep the checked date, because commercial features change after articles go live.

Use AI to fact-check AI content, but not to approve sources

Provide the draft and the source text together. Then ask AI to find the smallest passage that supports each claim and to flag mismatches in population, date, product tier or measurement.

The editor then verifies each match in the original source. Keep source excerpts short and internal. The published article should explain the finding in its own words and link to the source, rather than reproduce the ledger as a pile of long quotations.

Choose the right granularity

A ledger fails in two opposite ways. If it is too coarse, one row called "pricing" covers five statements that change independently. If it is too fine, the writer stops maintaining it after the second article.

A workable rule is one row per statement that could be wrong on its own. For instance, "The starter plan includes CSV export and API access" needs two rows, because a vendor can remove one feature without touching the other.

Some statements never need a row: definitions of common terms, your own recommendations and transitions. Others always need one: anything with a number, a product name plus a capability, a date, an eligibility rule or a description of automatic platform behavior.

Handle sources that disagree

Conflicting sources are where the ledger earns its keep. The temptation is to pick the newer page and move on. Instead, follow this sequence.

1. Record both: note each source with its date and exact wording.
2. Compare scope: check whether they describe the same plan, region or product version.
3. Escalate: look for a more authoritative source, such as a changelog, release note or the product itself.
4. Disclose: if the conflict survives, tell readers where the sources disagree and what to confirm.

Readers trust an article more, not less, when it admits a disagreement. What damages trust is a confident sentence that the vendor's own page later contradicts. And remember: AI can spot that two passages conflict, but it is never the tiebreaker.

A minimal ledger you will actually maintain

Tools matter less than habit. A spreadsheet with seven columns is enough, and these columns hold up well in everyday editorial use.

During drafting, reference rows inline, such as [C4]. Remove those markers before publishing, or keep them as hidden comments if your CMS supports that.

Turn the ledger into a refresh queue

When an article needs an update, update the ledger first. Rows whose checked date is older than the topic's usual rate of change become the refresh queue. That trigger works far better than a calendar reminder to "review old posts."

Also assign an owner and a review trigger to unstable facts. Then a pricing change, documentation update or broken source URL can trigger a recheck of just the affected rows, without rewriting the whole article.

Speed up extraction with a prompt

Once you have supplied the draft and the source material, this prompt fills a first version of the ledger. Use it only with those inputs in place.

Extract consequential claims from this draft. Match each only to supplied source material. Return claim ID, statement type, supporting location, mismatch, checked date and status. Leave evidence blank when absent.

Then check every row against the original source. The useful output is not a perfect-looking spreadsheet. It is a reliable answer to "What supports this sentence, and what would make us change it?"

Conclusion

To fact-check AI content reliably, give every decision-relevant claim a row with its source, location, date, scope and decision. Keep conflicts visible, let AI extract but never approve, and use stale dates to drive refreshes.

Start with your next draft. Build a seven-column ledger before you edit any sentence, and do not publish a claim until its row has a decision.

Sources

These sources informed the research for this guide. The checklist, examples and workflow are independently written and are not results of a SEOVision experiment.

• What AI Writing Tools Get Wrong (And The Stack I Use Instead): research starting point, not an endorsement of this workflow
• My Complete AI Content Process for Ahrefs: research starting point, not an endorsement of this workflow