GEO

AI search glossary: GEO, AEO and more

A plain-English AI search glossary: what GEO, AEO, LLMO, AI Overviews, AI Mode, fan-out, grounding, RAG and AI citations mean, and which ones cost you money.

By Ibrahem HelalAI-assisted, reviewed by Jeremy ZavaPublished 23 September 202616 min read

AI search is when an assistant such as ChatGPT, Gemini, Perplexity or Google's AI Overviews writes an answer instead of showing ten links. The words around it, GEO, AEO, LLMO, fan-out, grounding, RAG and citations, all describe one idea: getting your pages read, trusted and named inside that written answer.

This glossary defines each term in plain English, shows how they connect, and says which ones change what you pay for. If you already know the words and want the work done, our generative engine optimization service is where this glossary turns into a plan.

On this page

What does "AI search" actually mean?

AI search is any search experience where a language model reads sources and writes the answer for you. The model does not just list pages. It picks some, summarizes them, and sometimes links to the ones it used.

That covers a lot of products. Google has AI Overviews at the top of normal results and a separate AI Mode tab. OpenAI has search inside ChatGPT. Perplexity is built around it. Claude and Gemini can search the web while they answer. The interfaces differ, but the pattern is the same: a question comes in, the system looks things up, and a model writes a reply.

The key word in that pattern is "looks things up." Most AI answers about products, prices, vendors and how-to steps are not pulled from the model's memory alone. They are built from pages fetched at the moment you ask. That is why the terms in this glossary exist. If the answer is built from pages, then which pages get picked, and how they get named, is something a business can work on.

It also explains why the vocabulary grew so fast. Search marketers, AI researchers and product teams each named the same things differently, then the names leaked into sales decks. The result is a pile of overlapping acronyms. The rest of this guide sorts that pile into three groups: names for the work, names for the machinery, and names for the results.

A quick note on bias. We sell this work, so we have a reason to make it sound complicated. We are trying to do the opposite. Most of these words are simple once someone says what they point at, and a buyer who understands them pays less for fog.

What are GEO, AEO and LLMO, and are they different?

They are three names for the same job: making your brand one of the sources an AI system reads, trusts and names when it answers a question. The differences are mostly about who coined the term and which part of the job it stresses.

GEO stands for generative engine optimization. The term was popularized by a 2023 research paper from a group of academic researchers that tested how page changes affect whether content shows up in generated answers [2]. Their finding in plain terms: adding clear statistics, quotations and cited sources to a page tended to raise its visibility in the answers, while old tricks like stuffing keywords did little. Our short definition lives in what GEO means.

AEO stands for answer engine optimization. It is older than GEO. It started with voice assistants and featured snippets, where the goal was to be the single answer read out loud. People now use it for AI answers too. We break down where it came from in what AEO means.

LLMO stands for large language model optimization. It puts the model at the center and tends to include things like how your brand is described in the model's training data, not only in live search. See what LLMO means for the details.

If a vendor says its GEO is different from someone else's AEO, ask what the monthly work is. The answer will be pages, facts, mentions and measurement in some mix. The label does not change the deliverables. For the full alphabet soup, including older terms like SGE, the AI SEO acronyms list covers each one in a line.

What are AI Overviews and Google AI Mode?

AI Overviews are the AI-written summaries that appear at the top of some Google results, with links to the pages they drew from. AI Mode is a separate, chat-style Google search where the whole result is an AI answer and you can ask follow-up questions.

Both live inside Google Search, and both use Google's own index. Google's guidance for site owners is direct on one point that saves a lot of money: there are no extra technical requirements to appear in these features [1]. A page needs to be indexed and eligible to show a snippet in normal search. No special markup or AI file is required. The same guidance says the normal SEO basics still apply, such as making content easy to find through internal links and keeping important content in text form [1].

The practical difference for a business is space. An AI Overview sits above normal results and can push them down the page. AI Mode may replace the list of links for a searcher who stays inside it. Either way, a page that is not named in the answer gets less attention than it used to, even if it still ranks.

Before AI Overviews, Google tested the idea under the name Search Generative Experience, or SGE. You will still see SGE in older articles and some proposals. It is the same idea at an earlier stage. Our explainers on what AI Overviews are and what Google AI Mode is go further into how each looks and behaves.

People often confuse AI Overviews with featured snippets, the boxed quote Google has shown for years. A featured snippet lifts text from one page. An AI Overview writes new text from several. That difference changes what you optimize for, and we compare the two properly in AI Overviews vs featured snippets.

What is query fan-out, and why does it matter so much?

Query fan-out is when an AI search system takes your one question and splits it into many smaller searches, runs them at the same time, and combines what it finds into one answer. It is the most useful term in this glossary, because it explains which pages get picked.

Google names this technique in its own documentation. It says AI Overviews and AI Mode may use query fan-out, issuing multiple related searches across subtopics and data sources to build a response [1]. Other assistants behave in a similar way when they search, even if they do not use the same name.

Here is a simple example. Someone asks, "What should I pay for an SEO agency for a small online store?" The system may quietly search for typical agency prices, what a retainer includes, what small stores need, red flags in proposals, and how long results take. Each of those sub-searches returns its own pages. The final answer is stitched from the best page for each piece.

That changes how you should write. A single broad page that touches everything lightly may lose to five pages that each answer one sub-question clearly. A page with a direct answer near the top, a short table, and a clear heading for each question is easy for a system to lift from. A page that hides the answer under a long intro is not.

This is also why we tell clients to write headings as real questions. The headings on this page are the questions an AI system would likely fan out to when someone asks about AI search terms. The deeper walk-through is in what query fan-out is.

What do grounding and RAG mean?

Grounding means tying an AI answer to real sources instead of letting the model answer from memory alone. RAG, short for retrieval-augmented generation, is the common technical method for doing that: retrieve relevant documents first, then have the model write using them.

RAG was described in a 2020 research paper that paired a language model with a search step over a large document collection [3]. The idea was simple and powerful. A model's memory is fixed at training time and can be wrong. If you fetch fresh documents and hand them to the model while it writes, answers get more specific and more up to date.

Grounding is the product-level word for the same goal. Google's developer documentation for its Gemini models, for example, offers "grounding with Google Search," which connects the model to live web content and returns the sources it used alongside the answer [4]. That returned list of sources is where the links in many AI answers come from.

For a business, the lesson is that retrieval is still search. Before a model can quote you, a retrieval step has to find you. That step looks a lot like normal search: it depends on your pages being crawlable, indexed, clearly written, and relevant to the exact sub-question. Grounding is why the phrase "SEO is the foundation of GEO" is more than a slogan.

It also explains a common surprise. Two assistants can give different answers to the same question on the same day, because they retrieve from different indexes or with different fan-out queries. There is no single "AI ranking." There are many retrieval systems, each with its own habits. Our explainers on grounding in AI search and retrieval-augmented generation and SEO cover each one in more depth.

How is an LLM different from a search engine?

A search engine finds and ranks existing pages. A large language model, or LLM, generates new text by predicting words based on patterns it learned during training. AI search puts the two together: the search engine finds, and the model writes.

That split matters because each half fails in its own way. A search engine can show you a bad page, but it will not invent one. A model on its own can write a confident, fluent sentence that is simply wrong. Grounding the model in search results reduces that risk but does not remove it. You will still see AI answers that misstate a price, confuse two companies, or cite a page that does not say what the answer claims.

For brands, this creates two separate jobs. One is being retrievable, so the search half finds your pages. The other is being described correctly, so the model half says accurate things about you. The first is mostly classic SEO. The second is about consistent facts across your site, directories, reviews and other places the model may read.

We go further into this split, including when each one is the better tool for a buyer, in LLM vs search engine.

What is an AI citation, and is it the new ranking?

An AI citation is a named link or source mention inside an AI-written answer. It is the closest thing AI search has to a ranking position, because it is the visible proof that the system used your page.

Citations look different across products. Google's AI features show links next to or under the answer. Perplexity numbers its sources. ChatGPT search shows sources you can open. Some assistants mention a brand by name without linking at all, which is usually called a "mention" rather than a citation.

Both count, but they are not equal. A citation can send a visit. A mention shapes what the buyer thinks of you even when nobody clicks. When we report on AI search for a client, we track both: how often the brand is named, how often it is linked, for which questions, and in which assistants.

One honest limit: citations move. The same question asked twice can cite different pages, because fan-out and retrieval are not perfectly stable. So a single screenshot proves very little. What matters is the share of answers that name you across a set of questions, measured over weeks. Anyone who promises a fixed number of AI mentions is promising something no one controls. The fuller definition sits in what an AI citation is.

What is zero-click search, and does it hurt my traffic?

Zero-click search is a search that ends without the person clicking any result, because the answer is already on the results page. AI answers make this more common, since a written summary often settles the question on the spot.

That sounds like pure loss, and for some queries it is. Simple facts, definitions and quick comparisons are the most likely to end without a click. If your traffic came mainly from those, you will feel it.

But zero-click does not mean zero value. A buyer who reads your brand name in an AI answer, then searches for you directly a week later, shows up in your reports as a branded or direct visit, not as a win for the page that got cited. That is why measuring AI search only by clicks undercounts it. We explain the measurement side in what zero-click search is.

The practical shift is to judge informational pages partly by whether they get you named, and to judge commercial pages by the enquiries they bring in. A definitions page that is cited everywhere and gets few clicks may still be doing its job, as long as the pages behind it convert the people who arrive.

Which crawler and bot names should I know?

AI companies send their own crawlers to read the web, and each one has a name you can allow or block in your robots.txt file. The names matter because blocking the wrong one can remove you from an AI search product without anyone noticing.

OpenAI is a clear example. It documents separate crawlers for separate jobs: one used to surface sites in ChatGPT search results, one used to collect data that may train its models, and one that fetches a page when a user's request needs it [5]. Blocking the training crawler does not have to block the search crawler. Many sites block all three by accident, often through a security plugin or a firewall rule nobody reviewed.

Google works differently. AI Overviews and AI Mode use normal Google Search crawling, so there is no separate bot to allow for them [1]. Google's separate AI control token governs use of content for training some of its other AI products, not whether you appear in Search.

The lesson is to know which bot does what before you change the file. Blocking a training crawler is a reasonable business choice. Blocking a search crawler is usually a mistake that looks like a traffic drop weeks later. If you want a checklist, our post on robots.txt rules for AI crawlers sets out a safe default.

Is SEO dead now that AI writes the answers?

No. AI search is built on top of search. The systems retrieve web pages before they write, and the pages they retrieve are the ones that are crawlable, indexed, relevant and clear. That is SEO.

What has changed is the shape of the result and how you measure it. Ranking third used to mean steady clicks. Now ranking third might mean being one of four sources in an AI answer that most people read without clicking. The work to get there is similar. The report that proves it worked needs new lines.

It is also true that some SEO habits are dead. Thin pages written only to catch a keyword, doorway pages for every city, and link schemes help less than ever, because a system that reads several sources and writes a summary has little use for a page that says nothing new. The original GEO research pointed the same way: adding real evidence to a page helped visibility more than adding keywords did [2].

So the honest answer is that SEO is changing, not ending. We make the full case, including the parts that did get worse, in is SEO dead.

What does an SEO or GEO retainer actually pay for?

A retainer is a fixed monthly fee for ongoing search work, usually with a set scope of pages, fixes, outreach and reporting each month. A GEO retainer is the same structure with extra work aimed at AI answers.

In practice, the extra work is small in number but specific. It includes checking how assistants describe your brand for a fixed list of buyer questions, fixing facts that are wrong or inconsistent across the web, rewriting key pages so each sub-question has a direct answer, and tracking citations and mentions over time. The rest is the same technical, content and authority work any good SEO retainer includes.

The trap to avoid is paying twice for the same work under two labels. If one agency bills for SEO and another bills for GEO, and both are rewriting the same pages, one of them is redundant. Ask each to name what it will ship in month four, by page. Our plain definition of the structure is in what an SEO retainer is, and our own plans and starting prices are on the pricing page.

How do I use these terms to check my own visibility?

You can run a basic AI search check yourself in an afternoon, using the vocabulary above. It will not replace a proper audit, but it tells you whether you have a problem worth paying to fix.

Repeat the same ten questions every month. The trend matters more than any single result, because citations shift from day to day. If the numbers stay flat after you fix the obvious gaps, that is the point where outside help earns its fee.

How do I explain AI search to leadership?

Keep it to three sentences: buyers now ask assistants before they visit websites, those assistants read the web to answer, and we want to be one of the sources they read and name. Every term in this glossary is detail under those three sentences.

Leaders do not need to know the difference between GEO and LLMO. They need to know what is at stake, what it costs, and how you will know it is working. So translate. Say "share of AI answers that name us" instead of "citation rate." Say "the searches the assistant runs behind the scenes" instead of "fan-out." Say "whether the assistant gets our facts right" instead of "entity consistency."

Then show evidence from your own market. The ten-question check above is ideal for this. A slide showing a competitor named in seven of ten answers while you appear in one is more persuasive than any definition.

Finally, be honest about limits. Nobody controls AI answers. The work improves the odds that you are retrieved, trusted and named, and the report shows whether the odds improved. That framing survives a skeptical finance team. A promise of guaranteed mentions does not. Our step-by-step version of this conversation, with a one-page outline, is in how to explain GEO to leadership.

What this glossary covers next

Each term above has its own short explainer. Here is where to go for each, in the order most readers need them.

• What GEO means gives the one-paragraph definition and where the term came from.
• What AEO means traces answer engine optimization from voice search to AI answers.
• What LLMO means explains optimization aimed at the model's own knowledge of your brand.
• What AI Overviews are shows how Google's summaries appear and pick their sources.
• What Google AI Mode is covers the chat-style search tab and how it differs from Overviews.
• What query fan-out is walks through how one question becomes many searches.
• Grounding in AI search explains how answers get tied to live sources.
• Retrieval-augmented generation and SEO connects the RAG method to everyday search work.
• What zero-click search is covers searches that end without a visit and how to measure them.
• What an AI citation is defines citations and mentions and how to track them.
• What an SEO retainer is explains the monthly structure most of this work is sold in.
• Is SEO dead makes the case for why search work still feeds AI answers.
• AI Overviews vs featured snippets compares the old answer box with the new summary.
• LLM vs search engine sets out what each one does well and badly.
• The AI SEO acronyms list defines every abbreviation you will meet in a proposal.
• How to explain GEO to leadership turns all of this into a short internal pitch.

Questions people also ask

• Are GEO, AEO and LLMO different things?

  Mostly no. They are three names for the same goal: getting your brand used and cited when an AI system writes an answer. GEO is the most common name, AEO leans on older answer-box work, and LLMO stresses the model itself. Buy the work, not the label.

• Do I need special schema or files to show up in AI Overviews?

  Google says no. Its guidance states there are no extra requirements to appear in AI Overviews or AI Mode beyond being indexed and eligible for a snippet in normal search. Structured data still helps search understand your pages, but it is not a secret key.

• What is the single most useful term to understand?

  Query fan-out. Once you know that AI search splits one question into many smaller searches, you understand why broad pages lose and why pages that answer one sub-question clearly get picked up and cited.

• Is SEO still worth paying for if AI answers the question?

  Yes, because the AI answer is built from search results. The engines retrieve pages before they write, so the pages that rank and read clearly are the ones they pull from. What changes is how you measure success, not whether the work matters.

• How do I explain all this to my boss in one sentence?

  Buyers now ask an assistant before they visit a site, the assistant reads the web to answer, and we want to be one of the pages it reads and names. Everything in this glossary is detail under that sentence.

Sources

1. AI features and your website — Google Search Central (checked 23 September 2026)
2. GEO: Generative Engine Optimization — arXiv (Aggarwal et al.) (checked 23 September 2026)
3. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — arXiv (Lewis et al.) (checked 23 September 2026)
4. Grounding with Google Search — Google AI for Developers (checked 23 September 2026)
5. Overview of OpenAI crawlers — OpenAI (checked 23 September 2026)

Find out which AI answers name your competitors instead of you

Book a free 30-minute consultation with Ibrahem and Jeremy. We look at your site while we talk, and you leave with a prioritised plan.

Book a free 30-minute consultation

Who wrote this

Ibrahem HelalCo-founder

Leads strategy and delivery.

Works on: SEO, GEO

Reviewed before publishing by Jeremy Zava, Co-founder.

Posts by Ibrahem Helal

More on this

• What Is GEO? A Plain DefinitionGEO, or generative engine optimization, is the work of getting a brand named and linked inside answers from ChatGPT, Perplexity and Google AI Overviews.
• AI SEO Acronyms: GEO, AEO, LLMO and MoreA 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.
• What Is Query Fan-Out in AI Search?Query fan-out is the technique AI systems like Google's AI Mode use to break one question into several searches, then combine the results into one answer.
• 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.