GEO and AEO name, in practice, the same discipline: getting your brand into the answers of ChatGPT, Gemini, Perplexity and Google's AI features. The difference between the two acronyms is one of origin and emphasis, not method.
This guide covers what you need to understand before any tactic: the mechanics of an answer, who defined each acronym, and the three layers of the problem. What works and what does not is covered separately, in the evidence behind GEO.
Start with the scene. An accountant at a small firm in Rosario opens ChatGPT and types: I need invoicing software for a six-person accounting firm, it has to work well with the local tax agency, which ones should I consider.
Four seconds later they have an answer with three recommended options and seven links at the bottom. They did not open Google, did not compare ten tabs, did not visit a single pricing page. If they click at all, it will be one click, probably on the option listed first.
For the companies in that category, three different things happened at once. Some were mentioned in the text. Some were cited: one of their pages appeared as a linked source below. And the vast majority did not appear, even though several rank first on Google. Everything people discuss when they say GEO, AEO or AI visibility is this.
What happens inside the answer
Between the question and the answer there are five moments. Almost every expensive mistake in this space comes from skipping this step.
1. Interpretation. The system reads the question and decides what is really being asked. Google calls this query fan out: one query opens into several subqueries that run in parallel, about tax-agency integration, pricing, reviews, comparisons.
2. Retrieval. With those subqueries, the system pulls pages: from a search index, from the live web, or both. This decides who gets on the field. If your page is not retrieved here, nothing you wrote matters afterwards.
3. Context. The retrieved pages are handed to the model along with the question. They are a handful, not hundreds. At this point your page competes with five or ten others, not with the whole web.
4. Generation. The model writes the answer using that material. This is where it gets decided what is named, in what words and in what order.
5. Citation. The system shows the sources with links, in an ordered list. The order is not cosmetic: the first one is the most visible and, in several interfaces, the only one shown without expanding the rest.
Two clarifications that save arguments. First, not every engine runs the five steps the same way: ChatGPT, Perplexity, Gemini, AI Overviews and AI Mode use different indexes and criteria. Second, a model can also answer without searching at all, from its training: no citations there, only model memory, and that is a different game.
The alphabet soup, and who defined each thing
The confusion is not yours: there are six names competing for almost the same thing, and only one has a paper behind it.
SEO is what you already know. Optimization for traditional search engines, where the result is a list of links and the goal is to rank at the top.
AEO, answer engine optimization, is the older of the two terms that survived. It was born in the era of featured snippets and voice search, when the fight was to win position zero, and it came out of SEO practice, not academia. Its core mechanic is still the same: write self-sufficient passages that answer a specific question in the first two sentences.
GEO, generative engine optimization, does have a precise origin. It was coined by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande in a November 2023 paper, with teams from Princeton, Georgia Tech, the Allen Institute and IIT Delhi. They defined a generative engine as a system that answers by synthesizing several sources with a language model, and GEO as the work of improving the visibility of your own content inside those answers.
AIO, LLMO, AISEO and company are later labels, with no shared definition and no literature. AIO is also confusing because many people read it as AI Overviews.
Here is the point almost nobody says out loud: the difference between AEO and GEO is one of origin and emphasis, not method. AEO comes from direct-answer surfaces, where a single source wins. GEO comes from research on how models pick sources to synthesize, where several win at once. The tactics converge because both reward the same thing: clear structure and passages that can be lifted without context.
And there is an institutional definition worth keeping at hand. In May 2026 Google published its official guide for generative features, and there it says that for its search engine AEO and GEO are SEO. It is the stance of an interested party and does not apply to ChatGPT or Perplexity, which do not share an index with Google, but it is the most explicit statement any platform has made about how it works.
We use GEO to name the discipline, because it is the only term with a published, citable definition. When someone tells you AEO and GEO are different things that require two separate programs, ask for the source. What really changes is not the acronym: it is the three layers of the problem.
The three layers almost nobody separates
Go back to the five steps above and notice there are three distinct goals hiding in there. Half the arguments in this space resolve themselves with this table.
| Layer | The question | Where it plays out |
|---|---|---|
| Retrieval | Can the system find your page and bring it into the context? | Steps 2 and 3. Indexing, crawling, ranking, authority |
| Citation | Once in the context, does it pick you as a source, and with which link? | Step 5. Passage relevance, specificity, format |
| Mention | Does your brand get named in the text, with or without a link? | Step 4. What the model knows and reads about you |
Almost every tactic being sold operates on layer two. Almost all the hard evidence says layer one weighs more. And layer three is measured with different tools and behaves differently: some brands get named all the time and almost never cited as a source, and the other way around.
Minimum vocabulary
Citation. Every link a model shows as a source. If an answer lists seven sources, that is seven citations.
Mention. Your brand named in the text of the answer, with or without a link.
Surface. Each distinct screen where a generated answer appears. AI Overview, the summary above Google's results, and AI Mode, the chat you move to when you follow up, are two surfaces of the same product and behave very differently.
Zero click. A search that ends without the person visiting any site.
RAG. Retrieval-augmented generation. The search-first, answer-second scheme almost every engine uses.
llms.txt. A community-proposed text file meant to give models a map of your site. There is no evidence for it as a visibility lever.
Structured data, or schema. Invisible markup in the HTML that describes what each thing on the page is. It works for rich results, not for getting cited more by AI.
Where to start if you are starting from zero
First, measure. Write down ten questions your ideal customer would ask a chat before buying. Not keywords, full questions. Ask them yourself in two or three models and note whether you appear, in what position and who appears in your place. That is your baseline.
Second, clear the technical floor. Clean, specific, stable URLs, HTTPS, indexability. Between 67.8% and 75.2% of citations point to internal pages two or more levels deep. It is not glamorous and it is a requirement.
Third, treat your site as one source among several. No brand domain exceeded 10% of its own category's citations in our data. Visibility is built as much in media, video and institutional references as in your blog.
Fourth, write product pages that answer before they persuade. They are between 22% and 31% of what any of the five models we measured cites, and ChatGPT's number one category.
Fifth, before buying a tactic, ask what experiment backs it. What replicated and what does not survive the data is in the evidence behind GEO. And to avoid swallowing the next headline, there is how to read a GEO study.
The first item on the list costs nothing. GEO Observer, our Chrome extension, captures your real searches in ChatGPT, Gemini and Google, free and with no usage limit. If you want continuous tracking, with a weekly scan of more than 100 questions and model-by-model comparison, it lives in AI Visibility.
First-party figures in this article come from Dashcrab GEO Citation Research 2026, with a data cutoff of August 11, 2026, and from the experiment of 518 searches captured with GEO Observer between July and August 2026. They are reported aggregated or anonymized, and the studies are observational. The full methodology is in the study PDF.