The AEO Content Checklist: 23 Things Every Article Needs to Get Cited

Twenty-three checks separate an article AI engines cite from one they skip. None of the checks require a redesign, a new CMS, or a six-figure content investment. They are small, structural moves applied to every article before publish. Hit all 23 and your article shows up as a source inside ChatGPT, Perplexity, Google AI Overviews, and Copilot. Miss two or three and you stay invisible.

I built this checklist by running every blog post on AEO Hunt through it before publication. The posts that pass all 23 items get picked up by AI engines within weeks. The ones that fail two or three usually do not, even when the writing is strong. AEO content uses the same writing you would do for a smart human reader. The difference is six structural details that let a language model lift your text cleanly into an answer.

The list is exactly 23 items because that is what I have validated against actual citation behavior across ChatGPT, Perplexity, Google AI Overviews, and Copilot over the past 18 months. It is not 30 items because the long tail past 23 produces diminishing returns. It is not 12 items because anything shorter leaves measurable gaps. Twenty-three is the working floor.

Why every article needs a 23-point checklist

An AI engine reads your article differently than a human reader does. A human scans, scrolls, follows their curiosity, and tolerates a slow opening. A language model has a budget. It scans the page, parses the structure, identifies the highest-confidence quote, and either includes it in the answer it returns to a user or moves on to the next source. The decision happens in milliseconds.

If your article fails the model's parse, it does not get a second look. There is no equivalent of "the article ranks on page two." Either the answer the model returns names your brand and quotes your page, or it does not. The middle ground in search results does not exist in AI answers.

What makes the difference is structural. Almost every site I audit at AEO Hunt has the same problem. The writing is fine. The strategy is fine. The technical setup is fine. What is missing is the structural detail that turns a useful article into an extractable one. The checklist below closes that gap.

The six phases at a glance

The checklist is grouped into six phases. Each phase corresponds to a different point in the article's lifecycle. Phases 1 and 2 happen before and during the draft. Phases 3 and 4 happen during editing. Phases 5 and 6 happen at publish.

The phases are: framing the piece around buyer intent, opening with a direct answer, structuring the body for extraction, formatting AI-friendly elements, building author authority, and wiring the entity graph. Each phase has three to four items. The full set is 23.

Phase 1: Before you write

Phase 1 covers the four checks that happen before you open a doc. Skip this phase and you write a polished article aimed at the wrong question.

1. Pull the live AI answer for your target query

Open ChatGPT, Perplexity, Google AI Overviews, and Copilot. Type the exact query your buyers would ask. Read the answer each one gives. The article you write has to be more useful than what is already there, or the AI has no reason to swap in your source. This takes about five minutes per query and gives you the actual benchmark to beat. If three of the four engines already give a strong, brand-cited answer, your article needs a genuinely new angle. If the answers are vague or contradictory, you have an opening.

2. List the brands AI is already citing

In each AI response, every named brand is a competitor. Write them down. These are the entities the model already recognizes in your category. To replace one, you need either a clearer answer, a more current data point, or a structural reason for the model to pull from your page instead. Knowing the incumbent list is how you write to displace it. It also tells you who the model considers an authority, which is information you cannot get from a backlink tool.

3. Write the buyer's exact question as your working title

If the query is "how do I get cited by ChatGPT," the working title is "How to get cited by ChatGPT." Resist clever titles for the first draft. Match the buyer's exact phrasing because that is what trains the model's retrieval pass to surface your article. You can polish the final title later, but keep the core question intact. Cute titles cost you visibility every time they obscure the question the article answers.

4. Pick the one thing you can say that the cited brands cannot

Every article needs a single piece of value that no incumbent source already provides. It might be an internal data set, a proprietary framework, a process diagram, a quote from a real customer, or a contrarian read on the question. Without that one thing, your article is a redundant copy of what already exists, and AI models reward novelty inside their citation pools. Decide your one thing before you write a word. If you cannot name it, do not write the article yet.

Phase 2: Open the article

Phase 2 is what an AI engine sees in the first 200 words. If the open does not give the model a clean, extractable answer, the rest of the article does not get read.

5. Lead with a 40 to 80 word direct answer

The first paragraph is the answer to the article's title question. No setup. No "in this guide we will look at." Just the answer, written in 40 to 80 words. Aim for a paragraph an AI model could quote verbatim and have it stand alone as a complete response. That paragraph is the asset. Everything else in the article supports it, expands on it, or proves it. If you cannot answer the title question in 80 words, your article does not have a clear thesis yet.

6. Add a definition block right under the H1

If your article defines a term, metric, framework, or concept, put the definition in a visually distinct callout under the headline. Use a div or blockquote styled with a colored border or background. AI models reach for clearly demarcated definitions because they parse as standalone, citation-ready chunks. Bury the definition in a paragraph and you lose the most quotable sentence on the page. The definition block also gives Google a clean candidate for featured snippets, which still matter.

7. Show both a publish date and a last updated date

AI engines favor fresh sources for recency-sensitive topics. Put the publish date in the byline. Add a "last updated" date if the article has been revised. Make both dates machine-readable inside a <time> tag with a datetime attribute. AI crawlers use those signals to decide whether your 2025 guide should beat a 2026 competitor on the same topic. Articles that look stale get filtered out before the model even reads the body.

Phase 3: Structure the body

Phase 3 is what the model sees once it descends past the opening. Body structure decides whether the rest of your article reinforces the lede or gets ignored as filler.

8. Match H2 and H3 headings to buyer phrasing

Read your headings out loud. Does each one match a phrase a buyer would say or type? "How to score your AEO maturity" beats "Scoring methodology." Question-format headings work especially well because they map directly to AI prompts. The model treats heading text as topical anchors when it decides which sections of your page to pull into an answer. If a heading is internal jargon, the model has no reason to surface that section.

9. Keep paragraphs under four sentences

Dense paragraphs are extraction barriers. Four sentences is a soft maximum. Three is better. Short paragraphs let an AI model isolate a single idea and quote it without summarizing half a wall of prose. If your paragraph runs seven sentences, the model is more likely to skip it and quote a cleaner source. Short paragraphs also keep the article scannable for humans, which lifts time-on-page and behavioral signals that feed back into ranking.

10. Use sentence case for every heading

"How AI engines pick sources" reads cleaner than "How AI Engines Pick Sources." Sentence case looks human-written, which matters because AI detection signals feed into how some platforms rank content. Title Case also signals press-release voice, which the model has learned to deprioritize. Sentence case throughout, including the page title and every subhead. Make this a hard rule.

11. Drop a key-takeaway block every 600 to 800 words

A key-takeaway is a short callout that summarizes the previous section in one or two sentences. Style it with a colored background and a left border. Place one roughly every 600 to 800 words. These callouts give an AI model an explicit summary it can lift without composing one itself, which improves the odds of a clean citation. Key-takeaway blocks are also where you should add speakable schema selectors, because they are usually the highest-confidence quotes on the page.

Phase 4: Format for extraction

Phase 4 is where most articles can move from invisible to citable in a single editing pass. The items here are mechanical. They take minutes, not hours, and the citation lift is immediate.

12. Use a comparison table whenever you have three or more items

Anything with three or more comparison points goes in an HTML table. Tables are the highest-signal structured format on a web page. AI models can read a table column header, row label, and cell value with near-perfect fidelity. The same comparison written in prose loses structure the model has to reconstruct, which it often does badly. If you have three plans to compare, three competitors to evaluate, or three frameworks to contrast, build the table. The extra ten minutes pays off every time the article gets pulled into an answer.

13. Turn step-by-step processes into numbered lists

Any process with more than two steps gets a numbered ordered list. Numbered lists give the AI an explicit sequence and an extractable count. Five steps in a numbered list is parseable. Five steps strung together with "first, next, then, after that" is not. Wrap multi-step processes in HowTo schema when you can, which adds another layer of explicit structure the model can use to lift the steps directly into a procedural answer.

14. Convert criteria, features, and traits into bullet lists

When you have three or more parallel items that are not sequential (features of a tool, traits of a good source, criteria for evaluating a vendor), use an unordered bullet list. Bullets signal that these items are siblings to the model, which is otherwise stuck inferring parallelism from prose patterns. Lists make the relationship explicit. They also force you to write tight, parallel phrasing, which reads better for humans.

15. Add a dedicated FAQ section with 5 to 8 questions

Every article needs a real FAQ section. Five to eight questions, each phrased the way a buyer would actually ask it. Answer each in two to four sentences. Wrap the entire block in FAQPage schema. AI engines treat the FAQ section as a dense citation source because every question-answer pair is already in the exact format the model uses internally to serve responses. The FAQ is often the single highest-citation section on the page.

Phase 5: Authority and proof

Phase 5 is the hardest phase to fake. It requires real authorship, real research, and real attribution. It is also the phase that separates articles AI engines cite consistently from articles that get cited once and then drift out of the citation pool.

16. Attribute the article to a named author with a bio

"Posted by Admin" is invisible. Articles cited by AI engines almost always have a named author with credentials. The byline links to an author page. The author page has a bio that establishes the writer as a person with a track record on this topic. If you are the only person writing for the site, the author is you, full name, every time. Anonymous publication looks like marketing copy. Named publication looks like editorial.

17. Cite at least three primary sources by name

A primary source is the original publisher of the data, study, statement, or document you are referencing. "Per Google's documentation," "according to OpenAI's developer announcement," "from the W3C spec." Three primary sources, each linked, each named in the body. AI models trace your citations to verify your claims, and the more anchored your article is to verifiable material, the more useful it is to the model as a citation source.

18. Include original data or a proprietary framework

The single hardest item, and the highest leverage. Run an internal survey, publish a methodology, name a metric, define a model, or share a dataset only you have. Articles AI engines cite almost always contain at least one piece of original material the model cannot find elsewhere. If your article is a rewrite of three other articles, you are training the model to cite the originals, not you. Even a single internal benchmark, properly labeled, lifts an article into the original-source pool.

19. Quote a real person on the record

A direct quote with a name attached signals primary research. Two sentences from a customer, an internal engineer, or a recognized expert on the topic. The quote does not have to be long. It has to be real, attributed, and ideally cross-checkable from a public profile elsewhere on the web. Quotes also give the article a human texture that prose-only writing lacks, and reviewers and AI detectors alike treat that texture as a positive signal.

Phase 6: Schema and entity graph

Phase 6 is the publish-day checklist. The items here are technical. They are also non-optional. An article without proper schema and entity wiring sits outside the structured data layer AI engines use to organize the web.

20. Ship Article, FAQPage, and Person schema, validated

Every published article carries three JSON-LD blocks at minimum: Article with author and publisher, FAQPage built from the FAQ section, and a Person reference for the author. Run all three through Google's Rich Results Test before pushing live. An invalid schema block is worse than no schema, because the model sometimes flags the entire page as malformed. Our schema markup for AEO guide walks through the exact JSON-LD templates AEO Hunt uses for every blog post, including the speakable selectors and the publisher Organization reference.

21. Add speakable selectors to your definition and key takeaway

Inside the Article schema, add a speakable property pointing at CSS selectors that mark the parts of the article best suited for spoken extraction. Typically that is the definition box, key-takeaway blocks, and H1. Speakable is a hint to AI engines and voice assistants about which paragraphs are the highest-confidence quotes on the page. It costs you nothing and explicitly directs the model to your best lines. Speakable selectors are one of the most underused properties in current schema implementations.

22. Link to and from three or more siblings in the same topic cluster

An article is not an entity by itself. It is a node in a topic graph. Every article should link out to three or more sibling pieces on related queries, and three or more siblings should link back in. This creates the internal entity graph that AI models use to figure out which sources are anchored in topical authority and which are isolated one-offs. The practical mechanics of cluster building, including how to choose anchor text and which siblings to prioritize, are covered in detail in our ChatGPT citation playbook.

23. Add sameAs entity links from the author to their public profiles

The Person schema for the author should include a sameAs array linking to the author's LinkedIn, Twitter, GitHub, public bio, podcast appearances, and any other verifiable presence. These cross-links are how AI engines establish that "Brendan Hunt at AEO Hunt" is the same entity as "Brendan Hunt on LinkedIn" and the same entity quoted in three other places. Without sameAs, the author entity stays orphaned and the article inherits zero authority from the writer. Add at least four sameAs entries on day one, and update the array every time the author gets a new public mention worth linking.

How to retrofit an existing article

The fastest way to use this checklist is on the articles you have already published. Pull your top ten content pages. Run them through the 23 items one by one and score each item as pass or fail. Most articles will fail somewhere between 12 and 18 of the 23 on the first audit pass. That number sounds bad until you realize that almost every site I audit looks the same on the first pass. The opportunity is in the retrofit, not in starting over.

Fix the cheap items first. Heading case, paragraph length, FAQ section, schema validation, dates in the byline, sentence-case titles. Each of these takes ten to thirty minutes per article and produces measurable lift inside two to four weeks for articles that were already close to the citation threshold. Cheap fixes are also where most of your team can contribute without writing new content. A copy editor with the checklist in hand can ship five retrofitted articles in a day.

Then work through the harder items. Original data takes longer because you have to gather and publish a dataset, a methodology, or an internal benchmark. Adding a real quoted source takes longer because you have to reach out and get permission. Wiring an article into a topic cluster takes longer because it requires writing or commissioning the siblings if they do not exist yet. These are the items that produce the biggest gains, and they are the ones most teams skip. Skip them and your retrofit work caps out at the floor. Do them and your strongest articles cross into the citable layer.

The order I recommend is: cheap items across all ten articles, then harder items prioritized by which articles already have the strongest content. A B-grade article cleaned up with the cheap items beats an A-grade article that is missing schema and a real author every time. Once the top ten are retrofitted, repeat the process on the next ten. Within two quarters, your entire content library can be running on the checklist.

The patterns that kill AI citations

Three patterns kill articles every time, and they show up across industries.

The first is the no-author article. A piece without a named author with a bio is invisible to AI engines that weigh author entity signals. Adding a real byline with a real bio often produces measurable citation lift within weeks, with no other change. The author does not have to be famous. They have to be real, named, and consistent across the site.

The second is the schema-without-content article. Brands sometimes try to fake authority by adding aggressive schema markup to thin content. Models catch this almost immediately because schema makes a claim about the content, and if the content does not back the claim up, the page gets devalued. Schema reinforces real content. It does not substitute for it.

The third is the orphan article. A page with no inbound internal links and no outbound links to related siblings reads as disconnected. AI engines treat connectivity as a signal of editorial coherence. An orphan article on a strong topic still gets skipped because nothing on the rest of the site supports it. Even three good inbound and outbound links from siblings can flip an orphan into a connected, citable node.

Avoid all three patterns and most of the checklist falls into place on its own. The 23 items are the standard. The patterns are the failure modes the standard exists to prevent.

Run a single article through the full 23 items the next time you publish. Use the existing draft, not a new one. You will catch four or five things you would have missed, and the article will ship in a meaningfully stronger position than the last one you published. Do it on the next article after that. By article four or five, the checklist is reflex, and your citation rate compounds across the whole library.