Editorial QA checklist for AI-assisted articles: review steps

Why AI-assisted articles still need editorial QA

AI can draft quickly, keep a neat structure, and turn rough notes into readable paragraphs. That’s the upside.

The trouble shows up right before publishing: accuracy, fit, and usefulness. A model can sound sure while being wrong, pull details from the wrong context, or flatten your voice into something generic. Publish that as-is and you pay later with corrections, lost trust, and pages that don’t perform.

Most problems fall into a few buckets:

• Incorrect claims (dates, numbers, definitions, product details)
• Tone mismatch (too salesy, too casual, or oddly formal)
• Thin SEO (missed intent, weak logic, vague headings)
• Unclear ownership (no sources, no accountability, no next step)
• Repetition and filler (a lot of words that say little)

A repeatable QA checklist beats editing “by feel” because it sets the same minimum bar every time. Reviews also get faster. Instead of re-deciding what “good” means on every draft, you check known failure points in a fixed order and fix what matters.

This matters for anyone shipping content without room for surprises: editors managing volume, marketers responsible for performance, and founders publishing thought leadership. It also matters if you generate drafts at scale with tools like GENERATED (generated.app). Faster generation helps, but it makes a consistent gate even more important.

QA is the last mile: the draft gets you most of the way there, and editorial review makes it accurate, on-brand, and worth reading.

Set standards before you review

Editorial review gets messy when everyone judges a draft by a different yardstick. Before you edit sentences, decide what this piece must do.

Start with the audience and the single job of the article. Is it helping a beginner choose a tool, helping a buyer compare options, or teaching a repeatable process? Pick one. If the draft tries to do three jobs, you’ll keep fixing symptoms instead of the cause.

Next, define the “must-be-true” facts. These are the details you won’t let drift during rewriting: names, dates, pricing, feature lists, and any numbers. If you mention a product or service, write the exact wording you want used and mark what needs a source.

Tone needs rules, not vibes. Write a few clear do’s and don’ts that match your brand. For example: do use short sentences and plain words; don’t use hype, vague promises, or “marketing fog.” If there are phrases you always use (or avoid), list them.

Then set the SEO basics up front: the primary topic, the search intent (learn, do, compare, fix), and the depth required. A quick checklist post shouldn’t read like a textbook, and a how-to shouldn’t be only opinion.

Finally, define “publishable” in plain terms. A draft is ready when the core claims are supported (or clearly labeled as opinion), the voice stays consistent, the intent is actually answered, the headings match what the page delivers, and there are no placeholders or unverified numbers.

A step-by-step review workflow that prevents surprises

A good workflow keeps you from polishing sentences while missing the big issues.

1. Skim for assignment fit. Does the draft match the audience, angle, and promise in the title? If it answers a different question than the headline suggests, fix the plan first.

2. Do a red-flag pass. Mark anything risky: hard numbers, dates, medical/legal/financial statements, product claims, and “best/always/never” language.

3. Fact-check only what matters. Verify each red flag and leave an internal note about what you checked. If you can’t verify it, rewrite or remove it.

4. Edit for tone and clarity. Remove filler, simplify phrasing, and make the voice consistent.

5. Tune headings and SEO. Tighten H2s, add missing subtopics, and remove awkward keyword inserts.

Finish with one read as a normal reader. Does it flow? Is anything confusing? Is the next step clear? Then confirm formatting and that the draft you reviewed is the one you’re publishing.

How to catch factual errors quickly

Factual errors hide in details that sound confident. The fastest approach is to turn the draft into checkable claims.

Do a fast claim sweep

Read once without editing and highlight anything that can be true or false: numbers, dates, rankings, definitions, and any strong promises. If you can’t check it, you can’t publish it as fact.

Start with the basics that readers notice immediately: proper nouns (people, brands, places), role titles, product names, and definitions. Then check time-based wording (“latest,” “recent,” “since”), and finally stats and comparisons (“top 3,” “#1,” “most popular”).

Watch for “sounds right” explanations

AI drafts often offer plausible reasoning that’s slightly off: mixing up cause and correlation, or putting steps in the wrong order. Any “how it works” paragraph should survive a quick test: would a subject-matter expert agree with each step?

Be strict about quotes. If a quote isn’t clearly sourced and verifiable, remove it or rewrite it as a plain summary without quotation marks.

If you’re generating drafts through a tool like GENERATED, treat the output as a starting point, not a source.

Tone and voice alignment checks

A draft can be factually fine and still feel wrong. Common issues are filler, hedging, sudden certainty, or a voice that drifts paragraph to paragraph.

Start by cutting padding: long warm-ups, vague claims, and repeated ideas. Then watch for absolutes. If something is framed as “always” true, either prove it, narrow it to what’s accurate, or remove it.

Shorter sentences fix more than you’d expect. For each paragraph, ask: what’s the one point? If a sentence doesn’t support it, cut or move it.

A simple test is to read the first and last paragraph back-to-back. If they sound like two different writers, the voice still isn’t settled.

SEO gaps to fix before the article goes live

SEO problems in AI drafts usually aren’t “missing keywords.” They’re mismatches between what the reader wanted and what the page delivers.

Name the search intent in one sentence and reread the draft like a rushed reader. If the main question isn’t answered quickly, rankings and engagement both suffer.

Headings do most of the work. Your H2s should sound like real questions or promises a reader expects, not clever slogans. If an H2 doesn’t clearly match what follows, rewrite it.

Also look for missing “expected” content. Many posts need at least one concrete example to make advice usable. How-to pieces need actual steps. Comparison intent needs a clear way to choose. Topics that trigger follow-up questions often benefit from a short FAQ.

Finally, clean up keyword usage. Pick one main term and use variants naturally. If keywords make a sentence sound weird, the sentence is the problem.

Safety, compliance, and brand risk quick scan

Even “safe” topics can hide risky lines.

First, hunt for accidental professional advice. AI drafts can slip into medical, legal, or financial instructions without meaning to. If the piece suggests a diagnosis, a legal interpretation, or tells readers what to invest in, rewrite it as general information or remove it.

Next, remove unsafe instructions and overpromises. Anything that could cause harm (or encourages bypassing safety features) shouldn’t be published. Marketing promises like “guaranteed results” and “works for everyone” should also go.

Then check privacy and guesswork. Remove personal data and avoid confident claims about real people, companies, or events unless you can verify them.

If you mention your own product, confirm you’re describing it accurately (what it does, what it doesn’t do, and what depends on outside factors).

Common mistakes editors make with AI drafts

The most common trap is treating an AI draft like a finished article and doing only a grammar pass. Clean sentences can still hide shaky claims, missing sources, or outdated info.

Another miss is letting the draft choose the angle. If the brief is “help beginners choose between two options,” but the draft becomes a generic trend piece, rewriting paragraphs won’t fix the core problem. Bring it back to the purpose: who it’s for, what they should do next, and what questions must be answered.

Also watch for polished sections that add no value: paragraphs that restate the intro, repeat a heading in different words, or list vague benefits without specifics. If it doesn’t teach, clarify, or support a claim, cut it.

Quick pre-publish checklist (fast pass)

When time is tight, do this short pass to catch the issues that cause the most damage after publishing:

• First screen: Headline, intro, and first subhead make the promise clear and deliver a direct takeaway.
• Proof check: Every stat, date, proper name, and quote is verified, noted internally, or removed.
• Heading integrity: Each heading matches what follows (no bait-and-switch).
• Voice consistency: No sudden hype, apologies, or stiff corporate phrasing.
• Intent match: The page answers the main question quickly and includes the expected steps or examples.

End with a clear next step for the reader. Don’t just stop.

If you publish through an API-based workflow (for example, generating and serving articles through GENERATED on generated.app), add one more check: confirm the exact final version is being served, with the final title, headings, and no placeholder text.

Example: reviewing a draft from start to finish

You get an AI-written post for a SaaS blog titled “How to Reduce Churn.” It reads well, but you run QA before it goes live.

On the red-flag pass, you see: “Teams that add live chat cut churn by 27% in 30 days.” There’s no source, and the number is oddly precise. You remove the stat and keep the useful idea in a way you can stand behind: reducing friction early in onboarding often lowers churn risk. If you truly want a number, you mark it as “needs verified source” and only include it after you confirm it.

Next, you find a vague case study: “One company improved retention with better onboarding.” That’s not actionable. You rewrite it into a concrete internal exercise: review your last 20 cancellation reasons. If “couldn’t set it up” shows up repeatedly, add a 3-step setup guide and a short in-app checklist.

Then you fix an intent gap. The post explains churn causes but never mentions early warning signs. You add a clear section on what to watch (usage drops, fewer key actions completed, repeat support tickets, billing friction), and you tie each signal to a practical next action.

Finally, you calm the tone. You cut lines like “Our solution will fix churn fast” and replace them with specific checks, changes to test, and what to measure next week.

Next steps: make QA repeatable

The best QA system makes the next draft easier.

Keep a shared QA template where the team already works (a doc, ticket, or CMS note). Keep it short enough that people actually use it, and clear enough that two reviewers make the same call.

After each publish, note what took the longest or caused the most revisions. If the same issue shows up twice, fix the source: adjust your brief, house rules, or prompts. For example, if drafts keep inventing statistics, make a simple rule: no numbers unless the draft includes a real source to verify.

If you’re producing content at scale, GENERATED (generated.app) can help with generating and polishing articles, translations, blog images, and CTA generation and tracking. The key is that the human QA gate stays the same: verify claims, align voice, match intent, and publish only what you can stand behind.