You have a draft. The facts check out, the voice is close enough, and it still gets ignored by every AI answer you care about. That is frustrating, and it is also normal.
Here is the good news: the problem is usually structure, not substance. What it takes to make AI content citable is smaller than it looks, and none of it involves rewriting your ideas. This guide walks you through a repeatable edit pass that moves the answer to the top of every section, makes each passage stand on its own, and gives engines like ChatGPT, Perplexity, and Google AI Overviews something clean to lift. This is the pass people mean when they say edit AI draft for AEO. You are not rewriting the ideas. You are moving the answers.
What you need: an accurate draft, the buyer questions it should win, a page editor, and somewhere to log what each engine says today.
One promise up front, and one honest limit. Better structure creates more chances for an engine to find and quote you. No edit guarantees a citation. Anyone who tells you otherwise is selling something.
Start with the prompt you want the page to win
Write down the exact question a buyer would type before you touch a single paragraph.
Not the keyword. The question, in the words a real person would use. Then write the page's one-sentence answer underneath it. That sentence is your anchor for everything that follows.
Here is the small worksheet to fill in:
Target prompt: the buyer's question, in their words Direct answer: one complete sentence Supporting sections: definition, process, conditions, comparison, next action Citation test: the exact prompt, and the platform you will check it on
Next, list the subquestions the page has to cover: what it is, how to do it, when it does not apply, what to do next. Give each one its own home. One primary intent per section, always.
Mark the buyer stage too. An awareness question and a decision question pull very different answers, and squashing both under one heading gives the engine nothing crisp to grab.
How you know it is done: you can state the target question, the one-sentence answer, and the supporting subquestions without rereading the draft.
Where people go wrong: they start by polishing the introduction. Or they treat a keyword as the answer. A keyword tells you which words might matter. It does not tell the reader anything.
Keep that prompt wording locked. You will run it again at the end, and if the wording drifts, the before-and-after comparison is worthless.
If you track a set of buyer questions already, this step is mostly bookkeeping. If you do not, that is the real gap. DeepSmith's AI Visibility Prompts area stores the questions you track, keeps per-prompt mention and citation rates, and holds the full answer history, so the prompt you edit against today is the same one you test against next month. Discover Prompts builds a starter set from your product, persona, and buyer-stage context when you are staring at a blank list.
Rebuild your headings around real questions
Descriptive headings are the cheapest structural win in citation-ready editing, and most AI drafts arrive with the vaguest ones.
Go through the outline and replace anything clever with something plain. A heading should name the question, task, or concept the section answers. "A new approach" tells nobody anything. "How to test whether a page is blocked" tells everybody everything.
Then fix the hierarchy:
- One H1, carrying the page promise.
- H2s for the major tasks or questions.
- H3s only where an H2 genuinely splits into distinct parts.
Put one intent under each heading. If a section answers what, why, and how in three unrelated paragraphs, split it or label the parts. And make the heading agree with the first sentence below it. A reader should be able to predict the answer from the heading alone.
Delete headings that create a false boundary inside one continuous argument. Fragmenting a single answer into five tiny H3s does not help anyone, human or machine.
How you know it is done: skimming the outline tells you what each section answers. The first paragraph under every H2 could be copied out on its own without the heading becoming a lie.
Where people go wrong: they keep the AI draft's headings because the words sound polished, and never notice that none of them name a question.
Bing's own AI performance guidance points in the same direction: descriptive headings, concise sections, tables, and FAQ-style content make information easier to understand and reference. Treat that as clarity advice, not a published ranking factor.
One guardrail while you are in here. Do not bolt on a special AI schema or an AI text file because you are optimizing headings. Google's guidance is explicit that AI features need no extra markup beyond normal Search eligibility and ordinary best practice.
Move the direct answer above the explanation
Under every H2, answer the question in the first sentence or the first compact paragraph. Then explain.
Most AI drafts do the reverse. They warm up with a definition, add industry context, walk through some history, and finally land the useful sentence in the last line of the section. That sentence is the one you want quoted, and it is sitting where nothing will find it.
So flip it:
- Direct answer first, naming the subject.
- Conditions and qualifiers immediately after.
- Context, rationale, and edge cases below that.
- The article introduction answers the whole question before it explains the process.
Repeat the core subject whenever a section changes topic. Do not make anyone infer what "it," "this," or "they" refers to.
Answer-first does not mean oversimplified. You keep the nuance. You just move it one sentence later, so the qualifier supports the answer instead of delaying it.
Common mistake: a polished summary at the end of a section is not answer-first editing. Move the useful sentence up. Leave the explanation where it is.
Is there evidence behind this? Some, and it is worth being precise about how much. A March 2026 CXL analysis mapped 100 Google AI Overview citations back to where they sat on the source page. Fifty-five percent came from the first 30% of the content, 24% from the middle stretch, and 21% from everything after. That is a small observational sample. It supports answering earlier. It does not prove that page position is an independent ranking factor, and it is not a reason to cut your article in half.
How you know it is done: paste the first paragraph under any H2 into a blank document. It still states the answer and names the subject.
This is also the habit worth building into production rather than repeating by hand. DeepSmith's Content Studio Writer applies AEO formatting during article creation, with crisp answers near the top and clear headings, so the structure you are fixing manually here arrives already built. It is a production aid, not a citation guarantee, and your editorial judgement still runs the piece.
Make every passage stand on its own
An engine rarely lifts your whole page. It lifts a passage. If that passage only makes sense because of the paragraph above it, it is not usable.
Run an independence edit on every important answer paragraph, bullet group, and table intro:
- Name the entity, subject, or action instead of opening with a bare pronoun.
- State the claim or instruction directly.
- Include scope, audience, timeframe, unit, or condition wherever leaving it out changes the meaning.
- Keep a rule and its exception in the same unit, or label the exception right below.
- Replace "as noted above," "the former," "this approach," and "the following."
- One clear claim or action per sentence. Split compound sentences that hide two answers.
- Keep the qualifier. An answer stripped of its "only," "usually," or "when" is not shorter, it is misleading.
Here is the difference in practice.
Buried and dependent:
This is especially useful when you need to improve visibility. Once the structure is in place, it can be applied across the rest of the draft.
Answer-first and self-contained:
To make AI content citable, place the direct answer under a descriptive heading and repeat the subject in the opening sentence of each important section. Apply the same pattern to the rest of the draft so every section can be understood on its own.
Same information. One of them survives being pulled out of the page.
Pro tip: use the blank-document test. Copy the heading, the opening answer, and the supporting block into a new file. If the subject or the condition vanishes, put it back before you worry about style.
Where people go wrong: they shorten passages by deleting the subject, the timeframe, or the qualifier. The paragraph gets tighter and less citable at the same time, because what is left is ambiguous.
Take it one section at a time. You will feel the rhythm after three or four, and the rest goes fast.
Turn dense prose into blocks an engine can lift
Formatting is not decoration here. It marks the boundary of an answer, which is exactly what a retrieval system is trying to find.
- Short paragraph for a definition or a direct answer.
- Numbered list for an ordered process or sequence.
- Bullets for parallel requirements, options, or checks where order does not matter.
- Table only when the reader compares the same attributes across several items. Give it a plain-language reading before or after, so the answer is not trapped in the cells.
- FAQ block only where the page genuinely holds discrete reader questions, each with a complete first sentence.
Two more rules that matter more than they look. Keep the important answer in real text, because an explanation that lives only inside an image is an explanation an engine cannot read. And break long paragraphs at a change of claim, condition, or action, not at every sentence.
Where people go wrong: everything becomes bullets. Tables restate the prose next to them. An FAQ block gets added as a checkbox with no real questions in it. Structure should reveal relationships, not sprinkle formatting on top.
A word on FAQ schema while you are here, because this is where people overreach when they edit for AI Overviews. Writing in a real question-and-answer shape helps a reader and an engine find discrete answers. Adding FAQ markup is not a citation lever. Google says no special schema is required for AI Overviews or AI Mode, the page still needs ordinary Search eligibility, and any structured data you do use should agree with what a visitor can actually see.
How you know it is done: you can point at the answer paragraph, the list or table supporting it, and the next action, without reading a wall of text. Every list item still means something read alone.
Add the context and access signals the passage needs
This is the last structural pass, and the one most editors skip. It is not fact-checking. It is making sure the page can be reached and understood.
Work through this list:
- Define a specialized term before its first use.
- Name the product, audience, and scope wherever the answer would otherwise read as generic.
- Put prerequisites and exclusions next to the instruction they qualify.
- Link to a deeper page from the sentence that creates the need for it, using descriptive link text.
- Keep key information in crawlable text, not only in a graphic.
- Confirm internal links make the page reachable from the rest of the site.
- Check the page is not blocked by robots.txt, noindex, nosnippet, data-nosnippet, max-snippet, or a CDN rule.
- Confirm your structured data matches the visible text.
- If you want to get cited by ChatGPT, check that OAI-SearchBot is not blocked.
That last one catches people out, so it is worth a sentence of its own. OpenAI treats OAI-SearchBot and GPTBot as separate crawlers. OAI-SearchBot is about appearing in ChatGPT search. GPTBot is about crawling that may feed model training. You can allow one and block the other, and a robots.txt change takes around 24 hours to show up in their systems. A decision about training access is not automatically a decision about search access.
Where people go wrong: they spend an afternoon perfecting a passage on a page that is set to noindex. Structure only matters after an engine can access and consider the page at all.
Do not rip out a control you did not set. Some of those tags are deliberate, and removing one without asking the site owner is how a structural edit turns into an incident.
Patience helps here too. Google says changes to preview controls can take anywhere from several days to several months to be recrawled and processed. An absence of citations next Tuesday proves nothing.
Test the edited page and record what actually changed
You cannot tell whether the edit worked without a baseline, so build one before you publish.
Run your locked prompt set on the target engines and log, for each one: the answer, whether your brand was mentioned, whether your page was cited, the exact URL that appeared, which competitor pages showed up, and the date.
After the edit is live, run the same prompts under the same conditions. Keep the platform, wording, locale, logged-in state, and search mode as consistent as you can.
Then read the answer and the source list separately, because they tell you different things. A mention is the engine naming your brand. A citation is the engine visibly linking your page as a source. You can get one without the other, and blending them hides what is really happening.
Per platform, here is what to look at:
- ChatGPT: if the goal is to get cited by ChatGPT, check whether your page appears in the cited sources, and whether OAI-SearchBot can reach it. Referral traffic is a separate downstream signal, and OpenAI tags those referral URLs with
utm_source=chatgpt.com. - Perplexity: read the source cards separately from the prose. Perplexity searches the web in real time, so repeat the test and log dates instead of trusting one response.
- Google AI Overviews: when you edit for AI Overviews, check the exact query and whether an Overview even triggers, because often it does not. Search Console reports AI-feature activity inside the Web search type of the Performance report, which is a signal, not a page-level citation ledger.
- Bing and Copilot: Total Citations, Cited Pages, grounding queries, and Citation Share are useful for trend reading. That data is sampled and aggregated, does not show every prompt, and measures none of ranking, traffic, authority, or quality.
Change one major structural variable at a time when you care about attribution. Change six, and all you have is an observation.
Two findings worth holding in mind while you read your results. An Ahrefs analysis published on March 2, 2026 compared 863,000 keyword SERPs against 4 million AI Overview URLs and found that about 38% of cited URLs also appeared in the top ten for the same query, with the rest spread lower down or outside the top 100 entirely. That does not make classic ranking irrelevant, and it certainly does not mean an unranked page will get cited. It means editing only for your blue-link position is an incomplete plan.
The second finding is a useful reality check on what structure can and cannot do. A Seer study run mostly in March 2026 looked at 15,783 prompts across four funnel stages and four platforms, and reported Trustpilot citation rates of 57.9% on ChatGPT, 51.9% on Perplexity, and 48.9% on Google AI Mode after controlling for brand size and organic strength. Review profiles correlate with higher citation rates. Paragraph structure is a different lever, and no amount of citation-ready editing substitutes for external trust signals.
How you know it is done: you have a dated before-and-after record, you can say whether you got a mention or a citation, you know which passage was used, and you have not mistaken a traffic bump for an AI citation win.
Doing this by hand for one page is fine. Doing it for forty is where it falls apart, and it is the part DeepSmith was built to carry: tracked prompts, mention rate, citation rate, share of voice, page-level citation attribution, per-platform breakdowns, competitor citations, and full answer history in one place. Coverage follows your plan, so it is worth being exact. Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and Enterprise or Custom covers all ten engines.
What to do next
Do not run this on your whole library. Pick one page that matters, ideally one already ranking but never cited.
Record the baseline. Run the seven passes. Publish. Retest the same prompts in a few weeks and write down what you see. That is the whole edit AI draft for AEO routine, start to finish.
That is one page and maybe an hour. The second one takes half as long, because by then the edit is a habit rather than a project. Momentum matters more here than perfection.
When you are ready to stop doing the tracking and the structural pass by hand, start a free DeepSmith trial and see real data and real drafts before you pay. It is 7 days, with no long-term contract.
