AEO vs SEO vs GEO vs LLMO vs AIO
SEO optimizes for search results; AEO, GEO, LLMO and AIO are overlapping labels for optimizing for AI-generated answers. GEO has an academic origin (a 2023 paper), AEO dates back to featured snippets and voice search, and LLMO and AIO are informal industry terms. In practice they describe the same work, which Google calls "still SEO".
Comparison table
| Term | Stands for | Origin | Primary target | Typical success metric |
|---|---|---|---|---|
| SEO | Search engine optimization | 1990s industry term | Ranked web results | Rankings, organic clicks |
| AEO | Answer engine optimization | Industry term from the featured-snippet and voice era | Direct answers: snippets, voice, AI answers | Citations, answer inclusion |
| GEO | Generative engine optimization | Named in the 2023 paper by Aggarwal et al.[1] | Generative engines that synthesize sources | Visibility inside generated responses |
| LLMO | Large language model optimization | Informal industry term | LLM chat assistants, with or without search | Brand mentions, accuracy of descriptions |
| AIO | AI optimization (sometimes “AI Overviews optimization”) | Informal; ambiguous | Google AI Overviews or AI generally | Varies |
“AIO” is ambiguous: some use it for Google’s AI Overviews, others for AI optimization broadly. Say which you mean.
What the vendors call it
- Google uses “AEO” and “GEO” only to say they are “still SEO,” and warns against third-party “AEO/GEO hacks.”[2]
- Microsoft Bing added GEO to its Webmaster Guidelines, defined as work on “content eligibility for grounding and reference in AI responses.”[3]
- Academia: the GEO paper formalized “generative engines” and proposed a benchmark and visibility metrics.[1] See research.
Where the practices overlap
All five share the same foundations: pages that can be crawled and indexed, content that answers the question, credible authorship, consistent entity data and third-party corroboration. The differences are mostly in emphasis:
- SEO weighs ranking factors across a whole query space.
- AEO puts the answer first and targets question-shaped queries (see content structure).
- GEO focuses on how a synthesizing model selects and quotes sources. The GEO paper tested edits such as adding citations, quotations and statistics.[1]
- LLMO also cares about what a model “knows” from training data, which you influence only indirectly through public coverage (see mentions).
Which term should you use?
Use whichever your audience understands, and define it once. This wiki uses AEO as the umbrella term because it covers AI answers, snippets and voice. When a vendor or study uses a different term, we quote it as written.
Red flags when a service is sold under these names
- Guaranteed citations or rankings. Neither Google nor Bing guarantees inclusion.[4][3]
- Claims of access to “internal” AI metrics. Google says “no third-party tool has access to our internal ranking or AI systems.”[2]
- Mass-producing a page for every fan-out variation, which Google says violates its scaled content abuse policy.[2]
- Selling llms.txt or “AI schema” as a visibility lever (see myths).
How Google defines the machinery behind the terms
Google's guide for generative AI features names two techniques. Retrieval-augmented generation (RAG), also called grounding, relies on Google's core ranking systems to retrieve relevant, up-to-date pages from its Search index. The systems then review the specific information on those pages to generate a response with clickable links to supporting pages. Query fan-out is a set of concurrent, related queries generated by the model to fetch additional results; Google's example is a query about fixing a lawn full of weeds that could fan out to searches such as "best herbicides for lawns" and "remove weeds without chemicals".[2] Vendor-documented
The practical consequence for the SEO versus AEO debate is that Google's AI features sit on top of the Search index. The same guide says AEO and GEO are terms for work on visibility in AI search experiences and that, from Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.[2]
What Google says you can ignore
The same guide lists tactics that circulate under AEO and GEO labels and states Google's position on each, for Google Search only:[2]
| Tactic | Google's stated position |
|---|---|
| llms.txt and other "special" files or markup | Not needed; Google Search does not use them. Creating them will neither harm nor help visibility in Google Search. |
| "Chunking" content into tiny pieces | No requirement; there is no ideal page length. |
| Rewriting content only for AI systems | Not needed; AI systems understand synonyms and general meanings. |
| Seeking inauthentic "mentions" | Not as helpful as it seems; core ranking systems focus on high-quality content and other systems block spam. |
| Overfocusing on structured data | Not required for generative AI search, and no special schema.org markup is needed; still useful for rich results. |
Two limits apply. These statements describe Google Search, not ChatGPT, Perplexity, Claude or Copilot, which publish their own guidance (see the engine comparison). And Google says third-party tools have no access to its internal ranking or AI systems, so advice should be checked against official guidance.[2] Vendor-documented
Choosing vocabulary for a project
For a team deciding which term to use internally, the sources suggest a simple rule. Use SEO for the shared foundation of crawlable, indexable, useful pages, because Google says its generative features are rooted in its core ranking and quality systems.[2] Use AEO or GEO for the extra work that is specific to AI surfaces, such as checking crawler access for each vendor, tracking citations and mentions, and testing prompts. That work is described on measuring AI visibility and AI crawler user agents. Practitioner practice
Frequently asked questions
Is GEO the same as AEO?
In practice, yes. GEO comes from a 2023 research paper about generative engines; AEO is an older industry term that grew from featured snippets and voice search. Both describe optimizing to be used as a source in answers.
Does Google use the term AEO?
Only to explain it. Google’s 2026 guide says optimizing for generative AI search is “still SEO” and lists AEO/GEO hacks you can ignore.
What is LLMO?
Large language model optimization: an informal term for improving how LLM assistants mention and describe a brand, including what they learned in training.
Does Google say AEO is different from SEO?
No. Google's guide for generative AI features says that from Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.
See also
References
Pages accessed September 28, 2026 unless a date is given. Sources added on October 31, 2026 were accessed that day. See all sources and our editorial policy.
- ^ "GEO: Generative Engine Optimization (arXiv:2311.09735; Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande)". arXiv / KDD 2024. Submitted November 16, 2023; revised June 28, 2024.
- ^ "Optimizing your website for generative AI features on Google Search". Google Search Central. Added May 15, 2026; last updated July 10, 2026.
- ^ "Bing Webmaster Guidelines". Microsoft Bing Webmaster Tools.
- ^ "AI features and your website". Google Search Central. Last updated December 10, 2025.