How to Check an AI Draft Before Publishing

An AI draft is ready when a reader can act on its advice without discovering that a key fact, prerequisite or step was invented. Fluent prose is only one part of that decision. Review the risky claims first, then test the instructions, and polish the language last.

Start with the reader's next action

Write down the one job the article should help someone finish. For a guide to exporting a report, that might be: “Export a date-filtered CSV and confirm that it contains the intended rows.” An introduction about the importance of analytics does not complete that job.

Highlight every sentence that changes a reader's decision: prices, limits, compatibility, numerical claims, required permissions and recommendations. Check those against current source material. A second model agreeing with the draft is not independent evidence.

Review in three passes

1. Evidence: attach a source or a documented observation to each consequential claim. Delete a claim you cannot support, or state exactly what remains uncertain.
2. Execution: follow the instructions using the permissions and starting state described. Record any missing step. If execution is unavailable, label the procedure as untested and avoid claiming that it works.
3. Editorial quality: remove repeated explanations, define unfamiliar terms and replace generic examples with a concrete decision the reader faces.

Keep factual and stylistic edits separate. Otherwise an editor can spend an hour improving a paragraph that should have been removed entirely.

Worked example: a misleading export instruction

Consider this hypothetical sentence: “Click Export to download every row in your account.” The interface may export only the filtered view. Rewrite only after checking the behavior: “Select the required date range, export the current view, and compare its row count with the filtered report.”

The improvement is not more elegant wording. It adds the prerequisite and a way to detect failure. If the exporter has a row limit, the article must explain how the reader can identify an incomplete file.

A practical release checklist

An article with an unresolved pricing claim should not pass because its overall writing score is high. Treat critical factual errors as release blockers. Minor wording preferences can wait.

Keep the review proportional

A short glossary entry does not need the same execution test as a migration guide. Increase review depth when a mistake would cost readers money, expose data or require substantial repair. Track recurring errors across drafts; those are better candidates for improving the brief than repeatedly correcting the same sentence.

Common ways a fluent draft fails review

The failures that reach readers are rarely grammatical. They are structural gaps that a smooth paragraph hides. Four patterns appear often enough to check for by name:

• The invented prerequisite. The draft says "open Settings and enable the export option" for a product where the option lives under a permission a normal user does not have. The sentence reads correctly and fails in practice.
• The silent version drift. Instructions match an interface that was redesigned. Nothing in the text is wrong for the old version; nothing in the text is right for the current one.
• The borrowed number. A percentage appears with no source, or with a source that measured something adjacent. The draft treats the figure as settled because it appears in several other articles.
• The unearned generalization. One documented case becomes "always" or "never". The evidence supports a single observation; the wording claims a rule.

Each pattern is easier to catch when reviewers know it exists. Add the ones your own drafts produce most often to the brief, so the next draft starts with fewer of them.

Decide who reviews what

A single reviewer reading start to finish tends to correct style and miss substance, because style problems are visible in every sentence and factual problems are visible only when you stop and check. Split the work by question rather than by section:

1. Factual reviewer: owns the highlighted claims. Confirms each against the current source, notes the date of the check, and records anything that could not be verified. This person does not edit prose.
2. Execution reviewer: follows the instructions in a matching environment, or states clearly that execution was not possible. This person reports missing steps and wrong assumptions, not opinions about wording.
3. Editor: applies the two reports, then improves clarity. The editor is the only person who changes sentences, which keeps the review record readable.

Small teams can have one person wear all three hats, but they should still do the passes in that order and keep notes for each. The order matters: a beautifully edited paragraph built on a wrong claim wastes the editing time.

Record the outcome, not just the verdict

"Approved" tells the next editor nothing. A short release note attached to the article carries the information forward:

The "unresolved" line is the most valuable one. It tells readers and future editors exactly where the article stops being certain, and it turns a vague worry into a specific question that can be answered later. An article with an honest unresolved line is more reliable than one that reads as complete and is not.

Over a few months these notes also reveal which sources go stale fastest and which kinds of instruction most often fail execution. That evidence belongs in the brief for future drafts, where it prevents the same error rather than catching it again.

Put this into practice

Copy the worksheet columns below into a spreadsheet and keep one row per item you check. The filled row is an illustrative example, not a reported customer result; replace it with your own verified records.

Use the following prompt only after supplying the records it requests:

Review the supplied draft against the supplied sources. Return claim, source passage, confidence limitation, reader consequence and proposed correction. Do not use model agreement as evidence. Mark unsupported claims UNKNOWN.

Research context

Editorial quality needs decision gates around AI drafting, rather than treating fluent output as completed work. The related Ahrefs starting points are How We Use AI for Every Article Without Making AI Slop and Google Doesn’t Punish AI Content; It Punishes Bad Content (331k Pages Studied). This guide’s checklist, examples and proposed workflow are independently written; they are not results of a SEOVision experiment.

Continue with the next task

• A Human–AI Content Workflow That Protects Quality and Trust
• AI Content Refresh: Update Evidence, Not Just the Date

Sources

• How We Use AI for Every Article Without Making AI Slop — Research starting point; not an endorsement of this original workflow
• Google Doesn’t Punish AI Content; It Punishes Bad Content (331k Pages Studied) — Research starting point; not an endorsement of this original workflow
• Google: helpful, reliable content — Primary documentation for the stated platform behavior
• Google: using generative AI content — Primary documentation for the stated platform behavior