A Practical Review Checklist for AI-Assisted Articles

An AI-assisted draft can be fluent, structured and still be wrong. A date may be outdated, a citation may point to the wrong document, or a technical procedure may omit a prerequisite. Editorial review should therefore focus on evidence and reader outcomes, not on whether the prose “sounds like AI.”

This workflow is for editors, authors and subject experts reviewing a draft before publication. It also works when a person wrote most of the article and used a model for a section, an outline or examples.

Write down the intended reader, their question and what they should be able to do after reading. Someone troubleshooting a deployment needs prerequisites and a way to verify the result. Someone comparing products needs relevant criteria and limitations. If the draft answers a different question, proofreading will not solve the main problem.

Assign responsibility for the article, factual review and publication. In a small team one person may hold several roles, but they must know when a subject expert is needed. Medical, legal, financial and other high-consequence advice deserves specialist review. If an essential instruction cannot be checked, do not publish it as a reliable procedure.

Set data boundaries before using outside tools. A draft may contain customer information, access tokens, unpublished contracts or internal incident details. Only share material with a service when you have permission and appropriate terms for handling it.

Read the entire draft and mark statements that could change a reader's decision: statistics, dates, prices, software versions, product features, quotations, requirements and claims about cause and effect. Treat “always,” “guaranteed,” “the only way” and unnamed studies as prompts for closer inspection.

Asking the same model to confirm its own answer is not independent verification. A second prompt may suggest questions or possible sources, but a human still needs to examine the evidence.

For each material claim, record the exact statement, source, date checked, reviewer and outcome. A link should support the claim itself; a company's home page or a search-results page usually does not establish a particular figure.

Prefer the original documentation for changing software behaviour and the original study and methods for research findings. Check whether the source covers the relevant region, population and period. Two articles repeating one report are not independent confirmations.

Open every link and check the relevant section. A functioning URL is not proof that the page supports the sentence. If a source has changed or disappeared, locate a suitable current source or remove the dependent claim.

Imagine a draft claims: “The new interface increases sales by 40%.” This is a fictional teaching example, not a measured result. Without a source, comparison period, method and audience, the statement is not usable as a general fact. If you locate the source, determine exactly what was measured and whether other changes occurred. If you cannot, remove the figure rather than asking the model to invent a substitute.

Check arithmetic separately from causal interpretation. A rise from 100 to 120 enquiries is a 20% increase relative to the initial 100. It does not by itself establish that an interface change caused the increase: traffic, seasonality or advertising may also have changed.

Run technical steps from start to finish in a suitable test environment. Record the software version, permissions, starting state, inputs and expected result. Confirm that button names and options match the relevant version; do not assume a procedure for one release applies to every release.

Review code for more than syntax. Check its dependencies, error handling and effects on data. Do not run unfamiliar commands on a production server. Use test data, prepare a recovery route and inspect destructive operations before execution. Never include live credentials in a sample.

Label invented case studies as hypothetical. For a real example, obtain permission, agree on which data can be published and make sure the conclusion follows from the measurements. Conceptual illustrations should not be presented as real product screenshots.

A search query expresses a task. For a how-to question, readers need conditions and actionable steps; for a comparison, they need criteria, tradeoffs and a way to choose. Check that the title and opening promise what the article actually provides.

Inspect headings, the title and description fields, internal links and meaningful image alternatives. Links should lead to existing, relevant destinations. Do not repeat a phrase mechanically or create multiple pages that offer the same answer without a distinct reader need. Good structure makes content easier to use and maintain, but it does not promise indexing or rankings.

Compare suspiciously specific passages with their sources. If material has been copied, investigate rights and attribution and prepare an original explanation where appropriate. A chart, photograph or screenshot is not automatically available for reuse because a search engine or model surfaced it.

Similarity scores can direct an editor's attention but do not prove that a statement is true or that rights are cleared. AI-origin detectors also cannot reliably establish who wrote a particular sentence. Do not make an editorial decision solely from a percentage or a detector label.

The author can prepare the evidence list and explain the reader's task. An editor can check structure and links. A subject expert can assess technical and high-risk statements. A publishing editor can compare the saved version with the reviewed version. Roles may overlap; the checks should remain visible.

For information that changes quickly, assign an owner and a review trigger, such as an updated specification, changed service terms or a scheduled content review. Keep the date a source was consulted and a note about the claim it supported.

Start with the claims that matter most to the reader. A fast draft becomes useful when the team can explain, verify and maintain what it publishes.

Russian original: https://morlix.ru/blog/kak-proveryat-ai-stati-pered-publikatsiey-fakty-primery-i-seo