An Editing Checklist for AI Drafts

Most editing checklists for AI drafts read like proofreading lists with an extra step bolted on for hallucinations. That misses the actual problem. An AI draft can be grammatically clean, evenly paced, and completely wrong, or accurate and completely unreadable, and a checklist has to catch both failure modes in the right order. Fix voice before you've confirmed the facts and you'll polish a paragraph you end up deleting. Fix facts last and you'll ship something fluent and false.

The order matters more than any individual item. Accuracy first, because a wrong claim wastes every hour spent after it. Structure next, because a good point buried in paragraph nine might as well not exist. Voice and specificity after that, once you know the piece is standing on solid ground. SEO and discoverability last, because there's nothing to optimize until the substance holds up. This editing checklist for AI drafts follows that sequence deliberately, not as a stylistic preference.

Start with the weak draft

Here's a paragraph a generic AI tool might hand back for a piece on remote onboarding, the kind of first draft most teams have seen:

In today's fast-paced business environment, remote onboarding has become increasingly important for companies of all sizes. There are many factors to consider when it comes to setting up a successful onboarding process, and organizations that leverage the right tools can streamline the experience for new hires. It's worth noting that a comprehensive approach can drive better outcomes and empower teams to hit the ground running.

Nothing here is false, exactly. It's also not useful. Every sentence could describe any company doing any kind of onboarding, which means it describes none of them well. The claims aren't checkable because there aren't any claims, just gestures toward a topic. This is what a draft looks like before the checklist runs.

Here's the same paragraph after the full sequence below:

Remote onboarding breaks down in the first week, not the first day. New hires get a laptop, a Slack invite, and a calendar full of meetings with people whose roles they don't understand yet. The fix isn't more documentation. It's a named point of contact for the first ten working days, someone whose job is to answer the dumb questions before they turn into a resignation.

Same topic, same length, a different piece of writing entirely. It has a position, that the failure happens in week one, not day one. It has a specific mechanism, a named contact for ten days, instead of a vague gesture at "the right tools." Every sentence would need to be individually deleted to be removed, because every sentence is doing something. That's the target. The rest of this piece is how you get from the first version to the second.

Check the claims

Accuracy comes first because it's the only pass where getting the order wrong costs you real time. Run this before touching a single sentence of prose, since a fluent draft can be confidently false with no tell in the writing itself.

Assume nothing and check everything that's checkable. Treat anything you can't check as a candidate for deletion. The deeper version of this pass, including how to trace sources and verify quotes, lives in our fact-checking guide.

The pass itself breaks into a short list:

1. Trace every statistic to a primary source, not a secondary summary that might already be wrong.
2. Independently locate every citation, because models fabricate references that look completely real on the page, complete with plausible titles and page numbers.
3. Confirm quotes for wording and attribution separately, since a model can get one right and the other wrong.
4. Check dates, names, and timelines specifically, because a year off or a reversed cause survives a casual read precisely because it's plausible.
5. Confirm anything time-sensitive, like prices, versions, or "the latest," is actually current as of today.
6. If a claim can't be verified, cut it. An absent fact beats a wrong one every time.

A short example makes this concrete. A draft on remote work once cited a "68% of managers" statistic with no source attached. Tracing it back turned up nothing matching that number in any survey on the topic. It got cut, and the paragraph was rewritten around a claim that could actually be verified. That's the whole pass in miniature.

Reuters Institute's research on automated fact-checking backs this up directly. It concludes that judgment and context remain out of reach for fully automated verification, and that human supervision will be needed for the foreseeable future (Reuters Institute).

Some newsrooms have gone further. One outlet reported by Reuters Institute now asks writers for annotated drafts as evidence of the work behind an AI-assisted piece (Reuters Institute), which is a heavier process than most teams need but shows where the industry is landing on verification.

Fix the shape

Structure is a separate pass because a well-organized piece of writing survives edits that a meandering one can't. Once the facts are settled, check whether the reader actually reaches your best material. A great point in paragraph nine is a point almost nobody reads.

Work through it in order:

• Start with the opening. Drafts default to throat-clearing, a paragraph of context before anything gets answered. The first two sentences should answer the core question directly, then the rest of the piece can expand on it.
• Check that each section owns exactly one idea. A section trying to do three things should split into three sections, or lose two of them.
• Look at the order too. Does it follow the sequence a curious reader would actually ask questions in, or the order the draft happened to generate them in? Those aren't always the same thing, and AI drafts default to the latter.
• Cut filler transitions, the kind that restate the last paragraph before introducing the next one, adding a sentence's worth of words and zero sentences' worth of meaning.

Google's own spam guidance is worth knowing here. It explicitly calls out content that's "stitched together" or generated at scale without adding value as the kind of thing its ranking systems are built to catch (Google Search Central). Buried points and padded transitions are exactly what that description covers, whether or not the page was written at scale on purpose.

More on structural editing specifically lives in our guide to editing AI content.

Make it worth publishing

Voice, specificity, and cuts work together, and the order between them matters less than doing all three. An AI draft's biggest problem usually isn't grammar. It's that the writing sounds like the average of everything ever written on the topic, because that's roughly what a language model produces, and average writing convinces nobody of anything.

Fixing voice means putting a consistent, deliberate register back into the piece and removing hedging where the evidence actually supports a direct claim. "This can sometimes potentially be a factor" asserts nothing. State what's true.

Take an actual position. Say which option is best, what's wrong, or what matters most, because refusing to commit to a view is the most generic move available to a writer.

Specificity is the fastest credibility upgrade you can make to a draft, and it's also the fastest way to catch remaining vagueness. "Many companies struggle with this" should become a specific version of the problem, described in enough detail that it's clearly about something. A generic onboarding draft becomes one about a five-person support team hiring its third remote employee in a year, with a manager three time zones away. That's a sentence someone can picture.

Where you have a real number, a named framework, or a genuine example, use it. Where you don't, don't invent one just to sound more specific. A fabricated case study just reintroduces the accuracy problem you already fixed.

Then cut. In the drafts we've run through this process, the first pass tends to run noticeably longer than the version that ships, and the excess is reliably the weakest material, not a random cross-section of it. Restated topic sentences, recap paragraphs, and conclusions that repeat the introduction are the usual suspects. Test every sentence against one question: does removing it lose anything real? If the answer is no, it goes. If your edit didn't remove a meaningful share of the words, you probably edited too gently.

Once the substance holds, check what's actually citable in the piece. This matters beyond the reader in front of you. AI answer engines like Google's AI Overviews and Perplexity work by extracting citable sentences and standalone claims from a page, not by summarizing the piece as a whole, so a definition or key point buried inside a longer sentence is invisible to that kind of extraction the same way it's invisible to a skimming reader.

OpenAI's own writing guidance recommends treating a first AI draft as exactly that, a draft, and iterating on it with specific instructions rather than vague requests to "make it better" (OpenAI). Its research also finds that most writing requests people send to ChatGPT are edits to existing text rather than requests to generate from scratch (OpenAI), which is a fair description of how this checklist gets used in practice.

A definition or a key claim stated as a clean, standalone sentence is what gets pulled into search snippets and AI answers, so look for the sentences doing that work and make sure they can stand alone without the paragraph around them. Descriptive internal links matter here too. A link with anchor text like generative engine optimization tells both the reader and the crawler what's on the other side, which a bare "click here" never does.