A useful AI content workflow keeps evidence beside each claim, so the editor doesn't have to reconstruct the research. Otherwise, the draft is finished but checking its statistics and untangling its citations takes the rest of the afternoon.
That gets harder when one agent researches, another writes, and a fresh conversation handles the edit. A qualification can disappear while the citation stays put.
Use one rule throughout: approve the claim and its evidence together, then record which draft you approved. If a rewrite changes the claim, reopen the review.
A shared document and a small table are enough to start. Docs checked 2026-10-24.
Give the article a job
Before asking for an outline, write down who the reader is and what the article should help them do. "Write about AI marketing" leaves too much undecided.
For an agency editor receiving AI drafts, the question might be: "Can I check this article without researching it all over again?" A completed claim table would help. Another list of writing tools probably wouldn't.
Keep the brief short:
| Field | Example |
|---|---|
| Reader | An agency editor checking AI-assisted client articles |
| Decision | Whether this draft has enough support to publish |
| Takeaway | A claim table and repeatable review procedure |
| Contribution | A completed example showing when approval must reopen |
Check whether you already have an article that does this job. If so, an update may be more useful than another page.
Build the claim table before the prose
A folder of links leaves the editor guessing which source supports which sentence. Give each substantive, checkable claim a row. Include its precise wording, source location, supporting evidence, limits, checked date, and status. Recommendations and interpretations should be recognizable as such.
Here is a completed example from Google's guidance for generative AI search, checked on October 24, 2026. It concerns llms.txt, a text file some sites provide to help AI tools find or use their content.
| Field | Completed example |
|---|---|
| Claim | Google Search ignores llms.txt. |
| Source location | The linked guide's "Mythbusting generative AI search" section |
| Evidence | Google states that Search ignores these files. |
| Checked date | October 24, 2026 |
| Limits | The statement covers Google Search, not every AI service. |
| Status | Supported |
Now change the sentence to "No AI system uses llms.txt." The source no longer supports it. The rewrite has stretched a statement about one service into a claim about every service. Keeping the link doesn't repair that mistake.
Record enough detail to catch similar shifts elsewhere. For a statistic, that means the sample, period, and denominator. For a product comparison, note the plans or versions tested and the conditions. If you describe your own test, retain the procedure and failures alongside the results.
Draft without filling the gaps
Give the writer the brief, claim table, original material, and voice guide. Ask it to preserve each claim's scope and mark new factual assertions NEEDS SOURCE in the working draft. It must not invent quotations, customer results, personal experience, or URLs.
For each gap, decide whether to research it, narrow the sentence, or remove it before publication.
Once the facts are in place, check what the reader can do with them. A product comparison, for example, could include a test readers can repeat. Label it as a proposed test if you haven't run it.
Cut paragraphs that contribute nothing beyond what the reader already knows.
Review the wording that will be published
Separate factual review from editing for clarity. Smooth prose can make a weak claim easy to overlook.
Give the reviewer the draft and evidence table, with this copyable prompt:
Compare each substantive factual claim in the draft with its cited source. Open the source and check the exact wording. Report the sentence, source location, verdict, and proposed correction. Flag unsupported details, missing qualifications, stale information, and conclusions stronger than the evidence. Do not fill gaps from memory. Mark inaccessible sources unresolved. Check the title and description too.
An agent can organize the checks, but a person should approve the result. Google's guidance on AI-generated content calls for manual fact-checking before publication, including the metadata. A second model agreeing with the first doesn't supply evidence.
Then read for usefulness. Can someone follow the instructions? Is a necessary step missing? Where does the article repeat itself? Fix those problems, then check whether the edits changed any factual claims.
Record approval against a specific draft version. Suppose version 3 says "Google Search ignores llms.txt." The reviewer approves that wording. Version 4 changes it to "AI search engines ignore llms.txt." That sentence needs another check because its scope has changed.
A short review note can record the draft version, reviewer, date, approved wording, and unresolved claims. Don't mark the article ready while unresolved factual claims remain in the copy.
Correcting a comma doesn't require repeating every check. Changes to numbers, scope, causality, capabilities, quotations, or recommendations do. Revisit affected claims when their underlying sources change, too.
Check the final page
Remove working labels and internal notes before release. Leave useful source links beside the claims they support. Check the rendered page on mobile, especially any tables readers need to use.
Use this short checklist:
- Does the article help a specific reader complete a task or make a decision?
- Can a reviewer trace factual claims to sources that support their exact wording?
- Does approval cover this draft, including the title and description?
- Have you resolved or removed every unsupported claim?
- Can readers use the examples and templates on the published page?
Try the process on one article and keep the claim table through its next revision. Record the time spent researching, drafting, checking, and correcting. That will give your team a basis for deciding whether the workflow saves effort across the whole job.
Keep the review with the work in Tokenrip
In Tokenrip, keep the brief, claim table, draft, and review as linked documents in the same workspace. Record the reviewed version and unresolved claims in the review document. Someone still needs to compare the approved wording with the final draft; storing the documents together doesn't perform that check.
For the handoffs, see how to manage multiple AI agents. To make the review instructions reusable across tools, see our guide to skills.
Connect me to Tokenrip. Fetch https://tokenrip.com/setup?ref=ai-content-workflow and follow it.