claude/agents/resources/content-shape-for-ai.md

Content shape for LLM extraction

How to write pages so AI engines quote, cite, and recommend them. Based on peer-reviewed GEO research (CMU KDD 2024, Aggarwal et al.) and tracked citation patterns across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews (2025-2026).

The six patterns that measurably increase AI citations

1. Definition Lead Architecture

Open the page (or first paragraph after each major heading) with:

[Entity] is a [category] that [differentiator].

Research backing: CMU GEO framework (KDD 2024) — pages with explicit definitional openings score significantly higher in LLM retrieval impression scores.

Good: "Astro is a static site generator that ships zero JavaScript by default, producing HTML at build time that search engines and AI crawlers can index without running a browser."

Bad: "In today's fast-paced digital landscape, choosing the right framework can feel overwhelming. At Acme, we know how important it is to..."

2. TL;DR / Answer Box above the fold

Insert an explicit summary block at the top of long content. AI engines preferentially quote from these blocks because the content is pre-summarised.

<aside class="tldr">
  <strong>TL;DR</strong> —
  Next.js 15 removes the pages/ directory entirely in favour of App
  Router. Migration requires rewriting route handlers, layouts, and
  data fetching. Estimated effort: 2-5 days for a medium project.
</aside>

CSS: no class requirement, but mark it semantically (e.g. aria-label="summary" or Speakable schema targeting this selector).

3. Question-then-direct-answer structure

Each H2/H3 heading phrased as a likely user query. First sentence after the heading: a single-sentence direct answer. Supporting detail follows.

Pattern:

## How much does a Qualibat RGE certification cost in France?

A Qualibat RGE certification costs between 500 and 1500 EUR for the
initial audit, plus an annual fee of 200-400 EUR. The cost varies by
trade category and company size.

[Detailed breakdown follows...]

Why it works: LLMs grade passages by answer-density relative to the query. A one-sentence self-contained answer has the highest density.

4. Citations and statistics (strongest measured lever)

Aggarwal et al., 2024 ("GEO: Generative Engine Optimization", KDD 2024) report that their optimisation methods collectively boost visibility by up to 40% in generative-engine responses, and state the effect varies across domains. Citations/statistics/quotations are among those methods.

Attribute this correctly. Until 2026-07-16 this section read "Adding peer-cited statistics with clear sources increases AI visibility by up to 40%" — pinning the paper's aggregate result on this one technique. The paper publishes no separate figure per technique. When quoting it to a client: "up to 40%, across the method set, domain-dependent" — never "+40% if you add stats".

Pattern: embed specific numbers with attribution.

Good: "According to the ADEME 2024 energy report, French households spent an average of 2,137 EUR on heating in 2023 — a 12% increase from 2021."

Bad: "Heating costs have increased a lot recently."

Source attribution matters: link the citation to the original source (<a href>), ideally with rel="cite". AI engines use link graphs to validate factual claims.

5. Structured lists and comparison tables

LLMs quote list items and table rows more readily than prose of the same content. Convert what you can:

Before (prose): "The best frameworks for public sites are Astro for static content, Next.js for dynamic server-rendered apps, and Nuxt for Vue-based projects."

After (list): "Best frameworks for public sites by use case:

• Astro — static content (blog, docs, portfolio)
• Next.js — dynamic SSR with React
• Nuxt — dynamic SSR with Vue"

Comparison tables are even stronger. Structure:

Framework	Rendering	Best for	JS by default
Astro	SSG + islands	Public content	0 KB
Next.js	SSG + SSR	Hybrid apps	Large

6. Freshness signals

Freshness is a real retrieval input: RAG systems fetch live and read timestamps, so a page updated this quarter carries a stronger recency signal than the same page last touched years ago. LLMrefs (a vendor, not peer review) reports cited content running ~25.7% fresher than organic top-10 across ~17M citations. Substantive updates only — bumping a date string is not freshness.

The "3x" that lived here was grafted from another claim. Until 2026-07-16 this read "Pages not updated at least quarterly are 3x more likely to lose AI citations (LLMRefs 2026 study)". LLMrefs' actual "3x" says brand mentions correlate ~3x more strongly with AI visibility than backlinks — a different subject entirely. No source supports a quarterly decay multiplier. Recommend quarterly refresh on its merits; do not price it with a borrowed number.

What to maintain:

• Visible "Last updated: YYYY-MM-DD" at the top of content pages
• dateModified in Article/BlogPosting JSON-LD (ISO 8601)
• HTTP header Last-Modified in sync with content change
• Changelog on evergreen reference pages

Do NOT fake dates — AI engines and Google increasingly validate freshness against actual content diffs.

Anti-patterns — what to avoid

Pronoun-heavy writing

LLMs resolve pronouns by context window, which costs them confidence. Prefer explicit entity names.

Bad: "It was founded in 2015. Its founders wanted to solve a problem. They saw that..."

Good: "Acme Corp was founded in 2015. Acme's founders, Jane Doe and John Smith, wanted to solve..."

Marketing fluff before facts

AI engines typically truncate retrieval windows. Fluff at the top wastes the budget. Put factual claims FIRST.

Bad (first 200 chars wasted): "In today's fast-moving digital landscape, businesses are constantly looking for ways to stay competitive..."

Good (first 200 chars dense): "Our API processes 50M requests/day at p99 latency of 47ms across 8 regions, with a 99.99% SLA. Pricing starts at 99 EUR/month for the 10K requests tier."

Claims without sources

Any numerical or comparative claim without a linked source degrades trust. AI engines can detect the pattern "number without citation" and weight those passages lower.

Cookie-cutter content across pages (especially city pages)

The 30/70 rule: when creating per-city or per-service variants, at most 30% of the content should be templated. 70% must be unique per page (local landmarks, specific testimonials, unique stats, real photos).

Generic city pages get filtered out as "doorway pages" by both classical search and AI engines.

Page templates by type

Service page (local business)

<h1>[Service] in [City] — [Business Name]</h1>

<div class="tldr">
  <strong>En résumé :</strong> [Business] offers [service] in [city + surrounding].
  [Key differentiator — price, response time, certifications]. Open [hours].
  Call [phone] or request a quote online.
</div>

<h2>What is [service]?</h2>
<p>[Service] is a [category] that [differentiator]. In [city], demand
is driven by [local factor — housing stock, climate, regulations].</p>

<h2>How much does [service] cost in [city]?</h2>
<p>[Specific price range] for a typical [job type], based on [n]
projects completed in [year]. Factors affecting cost: [list].</p>

<h2>Why choose [Business] for [service]?</h2>
<ul>
  <li>[Certification 1] — [what it means]</li>
  <li>[Certification 2]</li>
  <li>[N+ years] experience on [specific housing stock]</li>
</ul>

<h2>FAQ</h2>
[QAPage or FAQPage schema + visible Q&A]

Blog post / guide

<h1>[Clear, question-style or noun-phrase headline]</h1>
<p class="byline">By [Author Name] — Updated [Date]</p>

<div class="tldr">
  [3-5 sentence summary. Include the key number, the key conclusion,
   and any nuance.]
</div>

<h2>[Question 1]</h2>
<p>[One-sentence answer.] [Supporting detail with cited statistics.]</p>

<h2>[Question 2]</h2>
...

<h2>Sources</h2>
<ul>
  <li><a href="...">Source 1 — author, year</a></li>
  <li><a href="...">Source 2 — author, year</a></li>
</ul>

Homepage / landing

<h1>[Entity] is a [category] that [differentiator].</h1>
<!-- The H1 IS the Definition Lead. Yes, really. -->

<p class="hero-subtitle">
  [Elaboration on the H1. Include one concrete stat or proof point.]
</p>

[Primary CTA]

<section>
  <h2>What [Entity] does</h2>
  <p>[Functional description, one paragraph.]</p>
</section>

<section>
  <h2>Who uses [Entity]</h2>
  <ul><li>[Use case 1]</li><li>[Use case 2]</li>...</ul>
</section>

<section>
  <h2>How it works</h2>
  <!-- HowTo schema + visible steps -->
</section>

<section>
  <h2>Frequently asked</h2>
  <!-- FAQPage schema + visible Q&A -->
</section>

Self-audit — is this page AI-friendly?

• First sentence: [Entity] is a [category] that [differentiator] ?
• TL;DR or summary block above the fold ?
• Every H2/H3 phrased as a likely user question ?
• First sentence under each heading: direct answer ?
• At least 2-3 specific numerical claims with linked sources ?
• Visible "Last updated" date + matching dateModified in JSON-LD ?
• Lists or tables instead of dense prose where possible ?
• Entity names used explicitly, not pronouns ?
• If it's a city/service variant: ≥70% unique content ?