How to Make AI Cite You and Users Click YouDev Spec

How to structure a page so AI engines cite it as the authority and human visitors still have a reason to click. Hand this to whoever builds the page - every item below is a concrete, shippable decision.

The strategy in one sentence

Feed the AI the What and the Why to earn the citation, but reserve the How, the proprietary data, and the interactive experience for the human visitor. The gap isn't created by withholding the answer - it's created by offering something beyond the answer that only the page can deliver.

01 · Ideal page structure

1
H1 - the exact query or entity the page targets
One per page. Specific and answerable, not clever or abstract.
2
Introduction (50–100 words)
Answers what, why, and who it's for. Works for a human and an LLM at once.
3
Quick Answer block
2–4 sentences. The most extractable, quotable version of the answer - this is what gets cited.
4
Why it matters / core components
A bulleted or numbered list of the key factors. LLM crawlers extract structured lists aggressively.
5
The Pivot Point
The most important sentence on the page. Introduces the complexity, data, or tool that only a visit resolves.
6
The deep dive
Full methodology, step-by-step processes, data tables, embedded media, downloadable assets, interactive tools.
7
Original research / proprietary data
Its own section with a clear heading. Content the AI can't recreate from training data - outsized citation effect.
8
FAQ section
Clear question headings mapped to FAQPage JSON-LD. One of the most-cited block types in AI Overviews.
9
CTAs after major sections
Short, specific, tied to the depth just covered - referencing the gap you just created.

DOM scaffold (legible to Google, ChatGPT, Gemini, Claude, Perplexity, Bing)

<main>
  <section id="quick-answer"></section>
  <section id="explanation"></section>
  <section id="framework"></section>
  <section id="examples"></section>
  <section id="case-study"></section>
  <section id="original-research"></section>
  <section id="faq"></section>
  <section id="next-steps"></section>
</main>

02 · Technical foundation checklist

Semantic HTML5 architecture

Use <main>, <article>, <section>, <header>, <aside>, <nav> - not a flat stack of <div>s
Each content block sits in a meaningfully named semantic element the LLM can read from raw DOM

Headings as a knowledge map

Every H2 frames a specific user question or subtopic (not “Our Process” / “Overview”)
Every H3 narrows to one concrete point
Test: replace a heading with a placeholder - if meaning survives, the heading isn't pulling its weight

JSON-LD structured data

Article or TechArticle with headline, datePublished, dateModified, author, keywords
FAQPage schema for any FAQ section
BreadcrumbList for navigation context
Product / SoftwareApplication when the page discusses a product, plugin, or tool

EEAT signals

Named author with title, credentials, and a link to a bio/profile page
“Reviewed by” attribution for technical or data-heavy content
Visible datePublished AND dateModified, mirrored in Article JSON-LD
Outbound links to primary sources (studies, docs, papers)

Technical SEO baseline

Title tag: unique, 50–60 chars, primary query near the start
Meta description: 150–160 chars
Open Graph: og:title, og:description, og:image
Canonical URL declaring the authoritative version
Core Web Vitals: LCP < 2.5s, low CLS, responsive INP

AI-targeted summary block

Directly below the opening paragraph, add a Quick Answer section (id="quick-answer")
2–4 sentences, answers the core query directly, no hedging - this is the version that gets cited

03 · Content formula

Build value density toward the bottom. Layers 1–2 win the citation; layers 3–5 win the click. Don't invert this - never put original research up top where it's extracted on the first crawl.

Layer	Type	Share of page
1	Direct Answer	10%
2	Explanation	20%
3	Framework / Process	20%
4	Examples	20%
5	Case Studies	15%
6	Original Research	10%
7	CTAs	5%

04 · The five information levels

Facts. Direct, verifiable claims.

AI quotes verbatim

Interpretation. Your analysis of why the fact matters.

AI paraphrases

Experience. Operational context grounded in real work.

AI references, can't verify

Unique data. Proprietary statistics or original research.

AI points users to your page

Proprietary frameworks. Named methodologies and processes you built.

AI cites by name → visit required

05 · QA - mistakes that kill GEO performance

!Publishing only Level 1 facts the AI already has - give it nothing unique to cite you for
!Putting your best data in the introduction - it gets extracted on the first crawl pass
!Generic headings (“Benefits”, “Overview”, “Conclusion”) that carry no entity weight
!Missing or incomplete schema - forcing the AI to infer structure it could have read
!Referencing assets (templates, tools, benchmarks) without naming them in body text
!No named author, sourced claims, or visible dates - reads as unsigned content
!Treating GEO as one-time - update dateModified on every substantive change