How Should You Review AI-Written Copy Before It Ships?

What should you check before AI-written copy goes live?

Check claims before style. Every number, date, product feature, and quote must trace to a source you opened yourself. Then hunt for invented specifics, read for voice, and check the structure answers the reader's question. Grammar is the last and easiest pass, because language models rarely get grammar wrong and often get facts wrong.

Many review processes run in the wrong order. Someone reads the draft, fixes a few awkward sentences, swaps a word or two, and approves it because it reads well. Reading well is the problem. A language model writes fluent prose whether or not the facts underneath are real, so smoothness tells you nothing about truth.

I have published more than 1,000 articles on AI answer engines, schema, and E-E-A-T, so I think about this review pass a lot. What follows is the order I would use for any AI-assisted blog post, landing page, or email, whether it came from ChatGPT, Claude, Gemini, or a writing tool built on one of them.

Why do AI drafts need a different review than human drafts?

Because the failure modes are different. A human writer who does not know something usually leaves a gap or hedges. A language model fills the gap with something plausible: a statistic with a real-sounding source, a feature the product does not have, a quote nobody said. The errors look exactly like facts.

That changes what a reviewer must do. With a human draft, you mostly improve clarity and argument. With an AI draft, you first have to prove each factual sentence is true, because you cannot assume the writer knew. The confident tone of the draft is not evidence of anything.

It also changes where errors hide. They cluster in the most specific sentences: the numbers, the dates, the version names, the named companies and what they supposedly did. Those are exactly the sentences that make copy feel authoritative, which is why they are worth the most scrutiny.

How do you check the claims in an AI draft?

Build a claims list. Copy every sentence that states a fact about the outside world into a simple sheet, one per row. For each one, record the source, the URL, and the exact supporting text from that page. If you cannot open a primary source that says it, cut the sentence or rewrite it as clearly framed opinion.

Primary means the original publisher: the vendor's own docs or changelog, the regulator's filing, the research team's paper, the company's own newsroom. A blog post that repeats a number is not a source for that number. Neither is a summary inside an AI tool, even one that shows links, because the summary can misstate what the link says.

Pay special attention to statistics. Check that the number, the year, and the organization all match the source exactly. A common AI error is real number, wrong source, or real source, wrong year. If the original study cannot be found, delete the statistic. Do not soften it into studies show, because that keeps the false impression and removes the accountability.

What are invented specifics, and how do you spot them?

Invented specifics are details that make copy sound lived-in but were never true: I tested forty prompts, one client saw a 30% lift, it took me three hours. AI drafts produce them because they read as credible. Search the draft for numbers attached to first-person claims, and delete any that the author did not supply.

These are more dangerous than wrong statistics, because they put words in a real person's mouth. If your byline says you ran an experiment, readers believe you ran it. When it turns out you did not, the damage is to your credibility, not the model's.

Google's guidance is useful here. In a Search Central post dated February 8, 2023, Google said its ranking systems aim to reward original, high-quality content that shows expertise, experience, authoritativeness, and trustworthiness, however that content is produced. Invented experience is the opposite of experience. A reviewer's job is to keep only the experience that is real.

How do you review for voice without rewriting everything?

Read the draft aloud and mark only the sentences the named author would never say. AI voice problems are predictable: stacked adjectives, generic openers, hedging on every claim, and the same sentence rhythm repeated. Fix those patterns, keep the rest, and resist rewriting good sentences just because a machine wrote them.

A short voice sheet speeds this up. List the phrases your brand never uses, the words it prefers, the reading level, and two or three paragraphs that sound exactly right. Give that sheet to the writer and the reviewer. It turns a vague feeling of this does not sound like the brand into a specific edit.

Also check point of view. If the copy is first person, every opinion should be one the author actually holds. A model will happily invent strong opinions to make copy punchy. Strong is good. Borrowed is not. Ask the author to confirm any take that would surprise their peers.

What structural checks matter for search and AI answers?

Check that each section answers one question directly in its first sentences, that headings match how people actually ask, and that the page has one clear target query. AI drafts often bury the answer under context. Moving the answer up helps human readers, search snippets, and AI answer engines quoting the page.

Look at the headings first. They should read like real questions or clear statements, not clever labels. Then read only the first paragraph under each heading. If those paragraphs alone answer the page's main question, the structure works. If they are all scene-setting, rewrite them.

Then check internal links. AI drafts invent URLs that look right and lead nowhere. Every link should point to a page that exists and actually covers what the anchor text promises. I check this in code on my own site before anything is published, and I wrote about the human side of that habit in why I verify every fact before publishing.

Who should do the review, and when?

The person who owns the subject should review claims, and a separate person can review language. Facts need expertise. Style needs a fresh eye. Put the review inside the publishing workflow as a required step, so nothing reaches the CMS as live content until both checks are done and recorded.

If your content runs through a CMS like Webflow, make the review a status rather than a promise. Drafts land as drafts. A reviewer moves them forward. I described one way to set that up in how to set up a review step before CMS items go live. The tool matters less than the rule that nothing skips the gate.

Byline honesty belongs in this step too. Google's same post says publishers should consider accurate author bylines when readers would reasonably expect them. If a named person signs the piece, that person should have read it, agreed with it, and stood behind its facts.

If part of that review runs through an AI check first, model choice matters. I explain when a fast model is enough and when a reasoning model earns its cost in reasoning model or fast model for content QA.

How do you keep AI drafts from needing so much repair?

Fix the brief, not just the draft. Give the model your verified facts, your sources, your voice sheet, and a rule that it may not add numbers or personal claims you did not supply. A tight brief cuts invented content sharply, which shrinks the review from repair work to a confirmation pass.

The brief should say what the model may not do as clearly as what it should do. No statistics unless listed. No first-person experiences unless listed. No product capabilities unless backed by the docs you attached. Models follow explicit limits far better than general requests to be accurate.

I covered how to write that kind of instruction in how to write a content brief an AI writer cannot misread. Better input will not remove the need for review. It will make the review faster and far less painful. Outbound teams should also audit AI outbound against Anthropic's 2026 usage policy update.

What should you do next?

Take the last AI-assisted piece you published and run the claims pass on it today. List every factual sentence, find the primary source, and note what you cannot verify. Fix or unpublish what fails. Then make that claims list a required part of every new piece before it reaches your CMS.

It also helps to remember the line Google drew in that February 2023 post: using automation, including AI, to generate content with the primary purpose of manipulating search rankings violates its spam policies. A real review process is the difference between using AI as a drafting tool and using it as a shortcut.

If you want help building a content workflow where AI drafts move fast but nothing false gets published, reach out. I build websites and the automations behind them for lead generation, including the review gates that keep them honest. Let's chat.