Generative Engine Optimization (GEO)
GEO is the practice of optimizing a website so generative AI engines (ChatGPT, Claude, Gemini, Perplexity, Copilot) cite it when answering user questions.
AEO (Answer Engine Optimization)SEOChatGPT
Explore topic Knowledge Base
GEO & AEO Knowledge Base
Authoritative topic summaries and entity relationships for Generative Engine Optimization and Answer Engine Optimization. Each topic exposes its parts, related entities, and key facts so generative engines can extract and cite them directly.
Topics
Answer Engine Optimization (AEO)
AEO is the discipline of structuring content so that answer engines — Google AI Overviews, voice assistants, ChatGPT search — quote it directly.
GEOAEO vs SEOGoogle AI Overviews
Explore topic llms.txt
A plain-text Markdown file at /llms.txt that lists the highest-signal URLs on your site for LLM crawlers.
robots.txtsitemap.xmlllms-full.txt
Explore topic JSON-LD Structured Data
JSON-LD is the schema.org format AI engines use to identify entities, articles, FAQs, products, and organizations on a page.
schema.orgSpeakableJSON-LD generator
Explore topic AI Crawlers
AI crawlers are the bots — GPTBot, ClaudeBot, PerplexityBot, Google-Extended — that fetch pages to train and ground generative engines.
robots.txtUser-agent
Explore topic Entity SEO
Entity SEO establishes your brand, products, and people as recognized entities in the knowledge graphs that ground generative answers.
Knowledge GraphE-E-A-T
Explore topic Entity graph
How the topics connect. Generative engines use these relationships to disambiguate entities and decide which page to cite for a given query.
- Generative Engine Optimization (GEO) part of AI Search Optimization — related: AEO (Answer Engine Optimization), SEO, ChatGPT, Perplexity
- Answer Engine Optimization (AEO) part of AI Search Optimization — related: GEO, AEO vs SEO, Google AI Overviews
- llms.txt part of Generative Engine Optimization — related: robots.txt, sitemap.xml, llms-full.txt
- JSON-LD Structured Data part of Generative Engine Optimization — related: schema.org, Speakable, JSON-LD generator
- AI Crawlers part of Generative Engine Optimization — related: robots.txt, User-agent
- Entity SEO part of Generative Engine Optimization — related: Knowledge Graph, E-E-A-T
Continue with the glossary
For single-term definitions, see the glossary (24+ terms).
Use the reference library to make an implementation decision
Start with the topic matching your problem: crawl access, answer clarity, structured data or measurement. Read the definition and limitations before applying a checklist. GEO and AEO build on useful, accessible content; neither replaces search fundamentals or creates a guaranteed shortcut to citations.
Verify claims at the source
Use the llms.txt proposal for file structure, Schema.org for vocabulary and vendor documentation for crawler purpose. Google’s AI-search guidance does not require special AI markup. Structured data must match the visible page, and valid syntax alone is not a guarantee of search features or source selection.
Keep a record of what changed
Save the original URL, input, date and evidence before editing. Verify the deployed page or file, document the changes and repeat the same checks. Separate technical validity, correct answer representation and qualified visits; none should be used as a substitute for the others.
Evidence and next actions
| Check | Evidence to retain | Action |
|---|---|---|
| Access | Public status and useful response body | Fix barriers before rewriting |
| Accuracy | Visible facts and supporting primary sources | Correct unsupported statements |
| Markup | Valid syntax that matches the page | Remove invented reviews and properties |
| Outcome | Comparable responses and referral activity | Report observations without guarantees |
Implementation checklist
- Use the tool or guide matching the problem.
- Verify important facts against current primary documentation.
- Keep private data out of published files and support requests.
- Check changes on the real domain.
- Retain comparable before-and-after evidence.
A repeatable workflow
- 1Establish a baseline
Save the page URL, date, language, target market and exact questions before editing. Record what the page actually says and what each tested answer cites; a mention without a source link is not a citation.
- 2Make one coherent change
Resolve the clearest failure first, then check the published page rather than only an editor preview. Keep an implementation log so unrelated redesigns, campaigns or product changes are not mistaken for the effect of this work.
- 3Repeat and compare
Repeat the same questions and technical checks after deployment. Keep engines and measurement conditions consistent, report sample size, and inspect answer accuracy alongside citations. A single response is not a stable ranking.
More questions answered
- Does this guarantee an AI citation?
- No. A crawlable, accurate page can still be omitted. Retrieval, source selection and generated answers vary between products and requests; these checks improve the evidence available, not guarantee placement.
- How do I tell whether an improvement worked?
- Compare like-for-like checks and retain the underlying evidence. Track correct brand mentions, source-linked citations and qualified referral visits separately. Do not call a technical score a traffic forecast or attribute every change to one edit.