seo-aeo-geo

# AEO and GEO

Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Make content discoverable, extractable, and citable by AI search experiences.

This skill encodes principles. AI search products evolve fast. The principles age slower than the products.

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## When to use

- Optimizing content for AI overviews and generative answer engines
- Building or updating llms.txt
- Structuring content so AI assistants can extract and cite it correctly
- Future-proofing a site as search shifts from blue links to AI answers
- Auditing whether existing content is AI-friendly
- Adding signals that help AI assistants identify the site as a trustworthy source

## When NOT to use

- Traditional on-page or technical SEO (use `seo-onpage` or `seo-technical`)
- Keyword research (use `seo-keyword`)
- Off-page authority and link building (use `seo-offpage`)

This skill stacks on top of those. Strong AEO/GEO requires strong fundamental SEO underneath.

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## Required inputs

- The site or page to optimize
- The topic area or query types AI should cite the site for
- Access to inspect rendered HTML and structured data

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## The framework: 5 layers

AI search visibility comes from five stacked layers. Each layer compounds.

### 1. Extractable content structure

AI systems extract facts and pull citations from content. Make extraction easy.

- **Direct answers.** Open major sections with a definitive 1 to 3 sentence answer to the question that section addresses. AI extracts the first answer it sees.
- **Question-headers.** Use H2s and H3s phrased as questions when natural. Mirrors how people prompt AI.
- **Atomic facts.** When stating a fact, state it once, clearly, with the supporting context next to it. AI struggles when claims are spread across paragraphs.
- **Tables and lists.** AI parses these reliably. Use them for comparisons, specs, steps, and data.
- **Definitions early.** When introducing a concept, define it inline. Do not assume the reader (or AI) saw a definition three pages ago.
- **Numbered steps.** For procedural content, number every step. Avoid prose disguised as instructions.

### 2. Citation worthiness

AI cites sources it considers authoritative. Earn that consideration.

- **Original data.** Surveys, studies, proprietary research, internal benchmarks. AI prefers primary sources over restatements.
- **Specific numbers.** "Roughly 40 percent" beats "many." Specific stats with sources beat round-number generalizations.
- **Named experts.** Author bios with credentials, links to professional profiles, schema-marked-up Person entities.
- **Date stamps.** Publication date AND last-updated date, both visible AND in schema. AI heavily weights recency for time-sensitive queries.
- **Methodology disclosure.** When stating a finding, briefly note how it was reached. AI rewards transparency.
- **Citations of other sources.** Linking to authoritative sources you used builds reciprocal credibility.

### 3. Structured data depth

Schema is how you speak machine-readable language. AI assistants parse it heavily.

- **Schema.org types** appropriate to content (Article, FAQPage, HowTo, Recipe, Product, Organization, Person, LocalBusiness, etc.)
- **Required AND recommended properties** filled in (most sites only fill required, leaving signal on the table)
- **Person schema** for authors, with `sameAs` links to verifiable profiles
- **Organization schema** on the homepage with logo, contact, social links
- **FAQPage schema** for content with genuine question-answer pairs
- **HowTo schema** for procedural content
- **BreadcrumbList schema** site-wide
- Validates in Schema.org Validator AND Rich Results Test (some properties differ)

### 4. AI-readable accessibility

Beyond traditional SEO, AI tools need access patterns of their own.

- **llms.txt at the site root.** A markdown file at `/llms.txt` describing the site's content, key URLs, and what topics the site covers. See [`references/llms-txt-guide.md`](references/llms-txt-guide.md).
- **llms-full.txt** (optional) - a complete content dump for AI training and context, if the site permits it.
- **robots.txt allowing AI crawlers.** Decide explicitly which AI crawlers to allow (GPTBot, ClaudeBot, Google-Extended, PerplexityBot, etc.) or disallow. Do not block by default if visibility matters.
- **Clean HTML semantics.** Semantic tags (`article`, `section`, `nav`, `main`) help AI parse structure.
- **Avoid client-side-only rendering for critical content.** Many AI crawlers render less reliably than Googlebot.

### 5. Real-world entity signals

AI builds knowledge graphs and prefers entities with multiple consistent signals.

- **Wikipedia entry** if the brand or person qualifies for notability (do not force this; it requires genuine notability)
- **Wikidata entry** for the entity, with consistent properties
- **Consistent NAP** (Name, Address, Phone) across all citations
- **Brand mentions across multiple authoritative sources.** AI cross-references entity claims across the open web.
- **Social profile schema** linking owned profiles via `sameAs` properties
- **Reviews and reputation signals.** Aggregate ratings on Google Business, Trustpilot, industry-specific review sites where applicable

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## Workflow

1. **Audit current state.** Run the 5-layer framework against the existing site. Score each.
2. **Identify the priority queries.** What questions should AI cite this site for? List 10 to 20.
3. **Test current AI visibility.** Query each of the major AI products (those relevant to the audience) with the priority questions. Note which sources they cite.
4. **Identify gaps.** Is the site cited? On which queries? Why does it lose to the cited sources?
5. **Layer-by-layer plan.**
   - Fix extractable structure on top 20 priority pages
   - Add citation-worthy signals (original data, expert authorship, methodology)
   - Deepen schema implementation
   - Build/update llms.txt
   - Strengthen entity signals
6. **Implement and re-test.