GEO is the work of getting AI systems to find, cite, and describe your brand accurately in generated answers. What it covers, how it builds on the SEO you already have, and how to measure it.
Anirudh Agarwal7 min readIf you are asking what is generative engine optimization, the plain-English answer is simple. GEO is the work of getting AI systems to find, use, cite, and describe your brand or content accurately in generated answers. You are no longer competing only for a blue link. You are competing to become part of the answer itself. GEO sits next to SEO, but it changes what you optimize for and what you measure.
Picture a mid-market CRM company. It is invented, but the pattern is not. It ranks on the first page of Google for its category term, but when a buyer asks an AI assistant for CRM alternatives for a small sales team, the answer names four other vendors instead. The answer cites two review sites and one community thread, and the company is absent.
That tells you the answer was assembled from third-party pages where the company is not discussed. The gap is in the sources the answer drew from, not in the company鈥檚 own Google rankings.
The field still has an acronym problem. The original GEO paper on arXiv introduced the term as improving visibility in generative engine responses. Since then, publications, tool pages, and practitioner conversations have spread the same work across several labels.
In modern AI search, AEO and GEO are largely interchangeable. AI SEO and LLM SEO are alternative industry terms for substantially the same work. Use AI visibility for the measurable business outcome and GEO for the optimization discipline.
Here is how the terms line up:
| Label | What people generally mean | How we use it |
|---|---|---|
| AI visibility | The outcome: are you found, cited, mentioned, recommended, and described accurately across AI answer surfaces? | Use as the umbrella outcome term |
| GEO | The optimization work behind that outcome across AI answers and cited sources | Use as the main discipline term |
| AEO | Another industry label for the same work, usually phrased around answer engines | Treat as alternative terminology |
| AI SEO / LLM SEO | The same work under SEO-first or model-first language | Treat as alternative terminology |
Generative engines synthesize information from multiple sources into a direct response, as described in the original GEO paper and reflected in Google鈥檚 AI search guidance. In plain English, GEO works to get your page, your brand, or the third-party sources that mention you pulled into that response.
It shows up three ways. Your page gets cited directly. Your brand gets named in an answer that draws on a third-party article, review, forum thread, or video. Or your information shapes the answer even when nobody clicks through to your site.
Rankings and visits alone miss where the answer actually gets decided.
If you already know SEO, you do not start over.
The foundation is the same. Google鈥檚 guidance still points you back to indexing, snippet eligibility, unique content, and clear page structure. That is why most GEO work still starts with the same technical and editorial basics you already know from SEO.
What changed is the surface, the unit of success, and the reporting line.
The surface is no longer just a ranked list of links. It is a generated answer with cited sources, and success shifts from clicks to inclusion, citation, or accurate description inside the answer, while reporting shifts from rankings and sessions to appearance and citation by engine.
That is why GEO is not "SEO but with new branding," and it is also not a clean replacement for SEO. You still need strong pages. You also need to care about the sources AI systems rely on, how your brand is described off-site, and whether the answer presents you correctly when no click happens.
AI answers do not all come from the same place.
Some answers lean on model memory. A 2025 Social Science Research Council working paper based on roughly 14,000 conversation logs from the LMArena platform found that, in that sample and period, Gemini generated 34% of responses and GPT-4o generated 24% without explicitly fetching online content.
Answer path 路 What it leans on
Training data and prior knowledge
Indexed, snippet-eligible pages
Model knowledge plus live sources
Some answers use live retrieval. Google鈥檚 generative search features still start from pages that are indexed and eligible to show a snippet in Search, per Google Developers.
Many answers use both. The model knows the topic in general, then grounds the response with current sources when it needs fresher or more specific evidence.
One visible prompt can trigger hidden follow-up searches. Practitioners call this query fan-out. Your page does not need to match the exact words the user typed to get used for a supporting angle, a definition, a comparison point, or a current fact.
And the unit that gets reused is smaller than the whole page. Clear headings, tight sections, direct answers, tables, and short paragraphs are good editorial practice because they help readers and make a specific answer easy to find, and Google says chunking content for AI is not required.
Different engines also expose very different evidence. In the same SSRC working paper, based on that 2025 LMArena sample, Perplexity鈥檚 Sonar visited about 10 relevant pages per query but cited only three to four, and Gemini returned no clickable citation source in 92% of answers. If engines see different source sets and show different proof, you should expect different outcomes.
Keep your first GEO sprint small enough to complete.
Do not start with a giant rewrite of your whole site. Start with a controlled slice of questions, pages, and source signals that let you learn where the misses are really coming from.
Here is a practical first sprint:
If you want the broader measurement and execution framework after that first sprint, our AI visibility guide goes wider. For now, keep the question simple: are your best pages extractable, are your brand facts consistent, and do the outside sources in your market tell the same story you want the answer to tell?
GEO is not a secret way to force position one inside a chatbot. These systems do not behave like stable SERPs. In our AI citation volatility study, a seven-day sample across ChatGPT, Google AI Mode, Perplexity, and Gemini found that 69% of the sources behind the typical answer changed from one day to the next, and 84% of the domains cited for a question were used by only one of the four engines.
It is also not a license to chase shiny objects. You do not need llms.txt, special AI-only files, or custom markup just to be eligible for Google鈥檚 generative features. Google says so directly.
And visibility alone is not enough if the model states facts incorrectly or credits the wrong source. In a 2025 Columbia Journalism Review test, eight AI search tools were given excerpts from 200 news articles and asked to identify the article鈥檚 headline, publisher, date, and URL across 1,600 queries, and more than 60% of the answers were wrong.
So being named is not the same as being represented correctly, and you need to check whether the answer states the facts accurately and attributes them to the right source.
If your report begins with one blended score, fix the report.
Start with four questions. Did you appear? Were you cited? Were you recommended or just named? Which sources shaped the answer? Keep those answers separated by engine, by prompt, and over time.
Take the invented CRM company from the introduction. The brand did not appear, so it was not cited and not recommended. The visible sources were two review sites and one community thread. That tells you to start with the pages this answer actually put in front of the buyer.
Visible citations show which pages an engine displayed, not every source that influenced the answer, so treat them as evidence of influence and not proof of it.
Treat citations as an incomplete ledger, not the whole story. In the same SSRC working paper, Gemini showed no clickable citation in 92% of answers, and Gemini or Sonar left about three relevant websites uncited on the average query. You need the answer text, the visible citations, and the off-site source pattern together.
Your starter measurement stack should be simple:
If you would rather not run this by hand, GetMentions AI runs the fixed prompt set across engines and keeps the answer text and cited URLs.
No. GEO still depends on the SEO basics that make pages indexable, useful, and understandable, then adds answer inclusion, citation, and off-site source work on top.
No. Google says you do not need llms.txt or special AI-only markup for Google AI Overviews.
Because ranking and answer inclusion are not the same competition. An AI system may answer from model memory, retrieve a different passage, or prefer a third-party source. Good SEO helps you get in the pool, but extractable passages, current specifics, and stronger off-site corroboration decide whether you get used.
No, because these systems are probabilistic and the same prompt can return different sources from one day to the next. What you can do is raise the odds and measure the change over a fixed prompt set. Be wary of anyone promising a position inside an AI answer.
Founder & Head of Research
Anirudh Agarwal is the Founder & Head of Research at GetMentions AI. He has been involved in SEO and search marketing for over 16 years, specializing in digital PR, AI search visibility, organic growth, and search strategy. Anirudh鈥檚 work focuses on understanding how brands are discovered, cited, and recommended across AI search engines and answer platforms. Through original research, data studies, and hands-on experimentation, he helps companies make sense of the changing search landscape and build trusted visibility in AI-powered discovery.
One buyer prompt branches into several narrower searches, and each branch favors different page types. How to map the likely branches, score your coverage, and close the gaps that cost you citations.
AI visibility is splitting into two halves: measuring where you stand and changing what AI says about you. This is the complete playbook for both, with 2026 data.
