AI SEO Acronyms: GEO, AEO, LLMO and More
A plain-language reference for the AI search acronyms marketers actually run into: GEO, AEO, LLMO, RAG, SGE and the rest, with what each one really means.
GEO, AEO, LLMO, RAG and SGE are the AI-search acronyms marketers run into most, and they cluster into two groups: terms for the discipline of getting cited in AI answers, and terms for how those answers are technically produced. Nearly all of them describe overlapping ideas coined by different people around the same time.
This post rounds up the working definitions in one place; the AI search glossary covers the same ground with more detail behind each term.
On this page
The discipline: GEO, AEO and LLMO
These three describe the work itself — getting a brand named and linked inside an AI-generated answer, the way SEO describes getting a page ranked inside a list of links.
GEO — generative engine optimization. The term with the widest current adoption. It covers the whole practice: crawler access, page structure, and earning mentions on the third-party sources assistants read.
AEO — answer engine optimization. An earlier, close synonym for the same work, framed around the idea that a chat assistant is answering a question directly rather than listing results. Many writers use AEO and GEO interchangeably.
LLMO — large language model optimization. A third name for largely the same discipline, framed around the model doing the answering rather than the "engine" wrapped around it. Some writers use LLMO more narrowly, for the specific work of making content easy for a model to parse and cite, as distinct from the broader entity and mention-building work GEO usually includes.
None of the three has a formal definition body behind it, so expect to see all three used loosely, sometimes by the same publication in the same article. If a client or a colleague asks which one your agency uses, the honest answer is that the label matters far less than the work behind it — what you should actually be checking is whether crawlers are open, whether pages are structured to be quoted, and whether a real measurement plan is tracking mentions and citations on a schedule.
| Acronym | Stands for | What it actually describes |
|---|---|---|
| GEO | Generative engine optimization | Getting cited inside AI-generated answers, broadly |
| AEO | Answer engine optimization | Near-synonym for GEO, framed around answering directly |
| LLMO | Large language model optimization | Near-synonym for GEO, framed around the model itself |
| RAG | Retrieval-augmented generation | Fetching sources, then generating an answer from them |
| SGE | Search Generative Experience | Google's early, now-retired name for AI-generated search results |
The mechanism: RAG, grounding and fan-out
These describe how an AI-generated answer actually gets produced, underneath whichever marketing term is used to describe optimizing for it.
RAG — retrieval-augmented generation. A model fetches relevant passages from an external source before writing, rather than answering purely from training data. It is the most common technical pattern behind a grounded answer, and it is worth understanding on its own terms if your team is being asked to optimize for it.
Grounding. The broader goal RAG serves: connecting an answer to real, current sources so it can be checked and, often, cited, rather than relying only on what a model absorbed during training.
Query fan-out. The technique of splitting one user question into several underlying searches, then synthesizing the combined results into one answer — the mechanism Google has named specifically for how its AI Mode handles multi-part questions.
These three terms sit at different layers of the same stack, which is worth keeping straight when a technical conversation and a marketing conversation happen in the same meeting. Grounding is the goal. RAG is the most common way of reaching it. Query fan-out is a refinement on top of RAG specifically for breaking apart questions that actually contain several smaller questions at once, rather than one clean lookup.
The features: AI Overviews, AI Mode and SGE
These are specific, shipped products rather than disciplines or mechanisms, and mixing them up with GEO or AEO is a common source of confusion in meetings.
AI Overviews. Google's AI-generated summary box, shown above ordinary results for a subset of queries, built from several sources synthesized into new text with a handful of citation links attached.
AI Mode. A separate, fuller conversational search experience inside Google that a searcher opts into, built around query fan-out to handle multi-part questions across several underlying searches at once.
SGE — Search Generative Experience. Google's original name for its experimental AI-generated search feature during its early testing phase. Google has since shipped the shipped, public versions of this work as AI Overviews and AI Mode under their own names, so SGE now mostly appears in older articles describing the earlier experiment [1][2].
The comparison terms: SERP, CTR and zero-click
A few older SEO acronyms are worth including because they keep coming up in the same conversations, now applied to a changed landscape.
SERP — search engine results page. The page of results a search produces. Featured snippets, AI Overviews and ordinary ranked links can all appear on the same SERP at once, depending on the query.
CTR — click-through rate. The share of people who see a result and click it. AI Overviews and other on-page answer features tend to depress CTR on the queries they appear for, since the searcher's need is sometimes met without a click at all.
Zero-click search. A search that ends with no click to any website, because the answer already sat on the results page. It is closely related to CTR but distinct enough to warrant its own term, since a zero-click outcome can still carry brand value through a mention even without producing a session.
E-E-A-T — experience, expertise, authoritativeness, trustworthiness. An older Google quality framework, predating the AI-search wave by years, that keeps resurfacing in AI-search conversations because the same qualities that make a page trustworthy to Google's ranking systems also tend to make it a source a generation step is willing to cite by name.
Why does keeping these straight matter?
Because conflating a discipline (GEO), a mechanism (RAG) and a feature (AI Overviews) in the same sentence makes it hard for a team to agree on what they are actually being asked to fund or measure. "We need to do GEO because of RAG" is not a coherent sentence; "we need to optimize for citation because assistants use retrieval before they answer" is. The AI overviews vs featured snippets comparison and LLM vs search engine both walk through two of the more commonly confused pairs from this list in far more depth than a glossary entry allows.
If you are the one who has to explain any of this to people who fund the work rather than do it, explain GEO to leadership is written for exactly that conversation, acronyms included.
Questions people also ask
Are GEO, AEO and LLMO actually different things?
Barely, in practice. All three describe the same underlying work — getting named and cited in AI-generated answers — coined by different people around the same time. GEO is the term that has stuck with most agencies and publications.
Is SGE still a term worth knowing?
Mostly for historical reasons. Search Generative Experience was Google's early name for its experimental AI search feature; Google has since shipped this as AI Overviews and AI Mode, and SGE mostly appears now in older articles.
Do I need to memorize every acronym on this list?
No. A handful — GEO, RAG, and whichever assistant-specific terms your team discusses weekly — cover most working conversations. The rest are useful to recognize when you encounter them, not to actively use yourself.
Is there an official body that defines these terms?
No. There is no standards body for AI-search terminology, which is exactly why definitions drift and overlap between publications. This list reflects the most common current usage as of the date checked above.
Why do so many new acronyms keep appearing?
Because the field is young and several groups — SEO agencies, AI labs, individual writers — have each tried to name the same emerging discipline at roughly the same time, without coordinating with each other.
Sources
- AI features and your website — Google Search Central (checked 23 September 2026)
- Expanding AI Overviews and introducing AI Mode in Search — Google (checked 23 September 2026)
- Overview of OpenAI crawlers — OpenAI (checked 23 September 2026)
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Who wrote this
Jeremy ZavaCo-founder
Leads growth and client partnerships.
Works on: CRO, SEO
Reviewed before publishing by Ibrahem Helal, Co-founder.
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- LLM vs Search Engine: How They Actually DifferA search engine ranks an index of pages; an LLM generates an answer from training data and retrieval. The difference changes what optimization means.
- How to Explain GEO to LeadershipA short brief for pitching generative engine optimization to executives: the business case, the timeline, the budget question, and how to avoid overclaiming.
- What Is an AI Citation?An AI citation is a source link an assistant attaches to a claim in its answer, distinct from simply naming a brand without linking it.