What is GEO? Generative Engine Optimization and AI answer citations

GEO, short for Generative Engine Optimization, is the content, access and measurement work that helps brands and web pages get mentioned and cited as sources in answers from ChatGPT, Gemini, Perplexity and Google’s AI features. This GEO has nothing to do with geography, the geometric mean or National Geographic. When we write for a generative engineGenerative engineAn AI system that writes an answer to a question and, when it uses web search, provides links to its sources., the answer must sit on the page clearly, next to its evidence.

A page should show what a service is, who it is for and how it is delivered. We work in a fixed order: access and indexing checks, a quotable definition, consistent brand information, and query × engine measurement. We record a brand being named in an answer separately from a page being cited, and tie each content decision to which question is answered in which section.

What does GEO stand for and what does it mean?

GEO stands for Generative Engine Optimization. The term comes from the paper “GEO: Generative Engine Optimization” by Aggarwal and colleagues, published on arXiv, which studies how visible content is in the answers of generative engines. In marketing, the practice examines how a brand and its information appear in those answers.

In practice, Generative Engine Optimization means preparing a page clearly enough to become the source of an answer. Instead of copy that only praises a service, we give its definition, scope, delivery steps and evidence. A paragraph lifted out of the page should still show which service it describes and when the explanation holds.

For one business the priority is having its service definition reported correctly; for another, being cited in comparison questions, and the query list follows that need. This is also where a “geo seo” search finds its answer: we assess visibility in the results list and inside a generated answer together, but in separate records.

Are GEO, AEO, LLMO and “AI SEO” the same thing?

GEO, AEO, LLMO and “AI SEO” are overlapping terms for answer-focused visibility work with different emphases. GEO stresses the generative engine’s answer, AEO the direct answer to a question, LLMO visibility in large language models. AIO, optimization for AI, is used alongside them.

For a business searching “ai seo” or “llm seo,” the term alone does not define the work. We write down in which questions, in which tool and for which page we want to be cited, then set the answer structure, access rules and measurement method.

A proposal is read the same way: are the queries and pages defined, and how will citations be recorded? Here, GEO names content editing, bot access and regular visibility reviews done together.

How does an AI answer choose its sources?

An AI assistant that uses web search finds relevant pages, takes the information that answers the question from them, and adds source links to its answer. When we examine source selection, we first check whether the answer used search at all, then which page supports which statement.

The retrieved passage must answer the question directly. A section headed “How is GEO measured?” that only says why the service matters does not explain measurement, so every content check starts with the link between the heading and the first sentence.

According to Google Search Central’s “AI features and your website” documentation, AI OverviewsAI OverviewsAI-generated summary answers with source links that Google shows for some searches. and AI Mode have no separate optimization requirement. The basic condition is that the page is indexed and eligible to show with a text snippet. We keep this eligibility separate from the record of which page an answer actually showed.

OpenAI’s bot documentation also separates search from training: OAI-SearchBot is used for search, GPTBot for model training. Opening a page to search and allowing its use for training are different decisions. To understand an answer’s source, we read the bot’s role as well as its name.

The type of question changes the direction of the review:

1. A general knowledge question answered from what the model already knows → We record answers without search separately and give GEO priority to queries that use web sources.
2. A question that needs current, local or detailed information → We examine the pages retrieved and the passages quoted from them, keeping the evidence for each statement visible.
3. A “which agency” or “which tool” question → We check the brand list, the service definition and whether brand details are consistent across sources.

The concrete step is opening the source link and finding the sentence it supports. If the brand is named correctly but the service scope is stretched, we note that separately; if the home page is cited, we check whether the service page states the point more clearly.

What is the difference between GEO and SEO?

SEO focuses on a page’s visibility in search results; GEO focuses on the page’s information being used in an AI answer, the brand being mentioned and the page being cited. The technical foundation is shared; the events we measure differ. A position in the results list and a source link in an answer are never the same record.

The table describes priorities: SEO also needs clear, sourced answers, and GEO also needs an accessible page. A vague service definition confuses both the visitor and the system reading the page, so we fix the title, description and scope together.

Six of the 9 competing pages we reviewed had a section on SEO vs GEO; none of these comparisons had a measurement row. We picked the pages with Semrush organic ranking data and counted their words on the pages; the source is our review of Google’s first page in Turkey, September. To tie the distinction to measurement, we record brand mentions, source links and clicks in separate fields.

From Google’s side, no GEO without SEO means the index: in Google’s AI search features, a page that is not indexed is not shown as a supporting source. We apply Google Search Central’s condition within that scope and review access, indexing and snippet eligibility alongside content editing.

What is SEO and what does it do?

SEO stands for Search Engine Optimization: work on content, links and technical structure so that pages are accessible to search engines, understandable and able to match relevant searches.

On a service page, a title that explains the topic, copy that meets search intent and an indexable page belong to the same work. We read ranking and click data through this page-and-query relationship; a GEO review adds mentions and citations in AI answers. Technical preparation and content structure are covered in detail on our English blog.

GEO by platform: where does each AI tool take its sources from?

In GEO by platform, we follow each tool’s own description of search and access and record the sources shown in that tool’s answers. Google Search’s indexing condition is not a rule for every assistant. For the same question, the content review is shared, while bot checks and answer records are split by platform.

Google AI Overviews and AI Mode

The technical basis for being a source in Google AI Overviews and AI Mode is that the page is in Google’s index and eligible to show with a text snippet. Google Search Central states that these features need no additional requirements or special optimization; the existing SEO fundamentals apply.

In the “query fan-out” approach Google describes, several searches run for subtopics. So besides the main definition, we answer the follow-up questions on implementation, comparison and measurement in their own sections. Each subheading should meet a separate need, not repeat the same sentence in other words.

We record Googlebot in access checks, the direct answer and its source in content checks, and the Search experience in measurement, with AI Overviews and AI Mode in separate columns. Search Console counts these features under the “Web” search type; citation records come from our answer review.

Our guide what is Google AI Mode covers the experience inside Search in more depth. Page preparation focuses on indexing, snippets, content and access, not on a special GEO file.

ChatGPT

For ChatGPT search results, the relevant bot is OAI-SearchBot; OpenAI’s documentation manages it independently of GPTBot. You can allow OAI-SearchBot and block GPTBot, expressing search access and the training preference as separate rules.

ChatGPT-User visits a page at a user’s request. The search bot, the training bot and user-requested visits are reviewed separately, and we check through the relevant bot whether the page is open for search.

In content, passages name the service and its scope clearly; the answer to “how to do GEO?” shows access checks, the definition sentence, sourced data and measurement steps together. In the answer record, the sentence naming the brand and the page offered as a source are kept apart.

When we pair the usage framework in what is ChatGPT with a GEO review, we ask the same question in the same scope. If we add the brand name to the question, it becomes a separate query; an unprompted mention and an answer to a question that named the brand are never mixed.

Gemini

For Gemini, we separate Google-Extended, which covers training and grounding with sources, from Google Search crawling through Googlebot. According to Google’s crawler documentation, Google-Extended manages content use for training Gemini models and grounding some Gemini answers in sources.

The Google-Extended setting does not affect inclusion or ranking in Google Search. Because it also concerns source use in Gemini, we do not call it a training decision only; the permission note shows both training and grounding use.

Answers in the Gemini app are recorded separately from Google AI Overviews and AI Mode: the brand wording, the source link and the service definition reported. A source seen in one environment is not reported for another.

The guide what is Gemini covers how the product is used; here we cover content and access decisions. A fix starts from the source passage, restating who the service is for and what it covers, and the next check compares the answer with it.

Perplexity

In Perplexity, the bot that surfaces and links to pages in search results is PerplexityBot. According to Perplexity’s crawler description, it does not collect content to train AI foundation models, so we do not treat PerplexityBot permission as training permission.

Perplexity-User is a separate user agent that visits pages at a user’s request. In the bot review, we record which access serves search and which comes from a user request, and check PerplexityBot’s access and the link shown in the answer separately.

In content work, each claim and its evidence stay in the same section: which condition a number describes, which service a definition belongs to, which criteria a comparison uses. A number never stands without its explanation and source name.

We apply the source-checking method from what is Perplexity to our own visibility: we open the link and find the matching sentence on the page. Content fixes are tied to the missing question or scope difference seen in that record.

Claude

For Claude, search access, user requests and web content collection are described with different bots. In Anthropic’s documentation, Claude-SearchBot relates to the quality of search answers, Claude-User to visits at a user’s request, and ClaudeBot to collecting web content.

Blocking Claude-SearchBot or Claude-User can reduce a site’s visibility in user searches. So we do not give every bot the same rule just because its name contains “Claude”; we decide which use is allowed and state which bot each rule affects.

In the page review, the question heading matches the scope of the answer: a definition question does not get implementation steps, and each need has its own section with its source and condition.

The usage context in what is Claude stays apart from the visibility record, which holds the question tested, the time, the brand mentioned and the source page. After an access change, we check the bot’s status first, then the answer, so content and access changes are assessed separately.

Copilot

For Copilot, we record how the brand is described in answers to our selected questions, along with any source links. Bot names from other platforms and Google’s indexing conditions are not carried over to Copilot, and the tool’s name is written clearly in the review table.

We ask “what is measured in GEO work?”, for example, in the same form as in the other tools, logging brand appearance, page citation and the reported information in separate fields, and reading the original text behind any source link.

The content assessment follows this match. If the page only defines the concept, we clarify the method section; if the answer exists, we check that it sits under the heading, with a clear subject and its source attached.

The guide what is Microsoft Copilot covers how the product is used. In the GEO record, we set up a structure that lets us recheck the same query, storing the brand wording from each test with its query and answer.

How do you do GEO?

GEO starts by identifying the question a page will answer and writing the first sentence as the direct answer to it. We state the service name, what it does and which need it meets, then add scope, reasoning and source. The passage then makes sense even when separated from the rest of the page.

When editing a page, we follow five steps:

1. Definition sentenceWe write one sentence that answers directly under each question heading and keep the subject explicit.
2. Question headingsWe build each heading around the question pattern readers use and keep the explanation focused on that question.
3. Entity consistencyWe use the brand name, address and service description in the same form on the site and in other sources.
4. Bot accessWe check OAI-SearchBot and Googlebot access in robots.txt and assess training permissions separately.
5. Sourced dataWe put each figure next to what it measures and its source, so the statement is easy to verify.

“Results-driven solutions” alone does not explain a service. Instead, we write a concrete sentence with a clear subject: “Our company is an engineering firm that installs and maintains cold rooms for food producers in Izmir, Turkey.” This is a wording example; on a real service page, the sector, location and scope come from the business’s own information.

The sentence answers who does what, for whom and where. The next paragraph explains the scope; services the page does not offer are not added, the answer is not hidden at the end of the introduction, and two related services each get their own explanation.

In the entity consistency step, we compare the brand spelling, address and service description side by side. The site, the Google Business Profile and LinkedIn should describe the same business with the same scope. If one says consulting and another implementation, we first clarify what the business actually offers, then align the descriptions around that confirmed scope; identical copy on every channel is not required.

In the sourced data step, what a number measures, which condition it belongs to and where it comes from are written together. A source that describes one tool’s feature is not extended to every AI platform, and a number from our own method is not presented as an industry rule. The number of queries per service, for example, is the markention method.

For bot access, in robots.txtrobots.txtA text file at the root of a site that tells crawler bots which paths they may crawl., the line “User-agent: OAI-SearchBot” assigns the rule to the search bot, and “Allow: /” grants access from the root path. We check Googlebot access separately. Blocking GPTBot does not remove OAI-SearchBot’s search permission; this follows the role split in OpenAI’s bot documentation.

Google-Extended is a separate control for training Gemini models and grounding some answers in sources, while crawling for the AI features in Google Search runs through Googlebot. We decide permissions by writing down the purpose and the platform, not by bot names alone. The table in the bot access section maps the search versus training split by platform.

Which content formats work for GEO?

For GEO we use quotable definitions, question headings, sourced data, comparison tables and FAQs, choosing each format for the information need it serves. Instead of repeating one fact in five formats, the definition gives the concept, the table the difference, the steps the implementation and the FAQ the short answer.

Quotable definition

A quotable definition is a direct explanation whose subject and scope make sense on their own. In “This method supports visibility,” the method is unclear. “GEO is the work of getting content cited as a source in AI answers” gives both the subject and the work.

We first remove the heading and read the sentence alone. If it still makes sense, we check its scope: does it explain the whole service or one part? If the service targets a specific need, that condition belongs in the first explanation. Later paragraphs expand the definition with examples and reasoning.

Service pages follow the same method: the brand, the work and the audience are stated clearly, readers never scroll back to decode “this” or “the solution in question,” and a short definition keeps its essential condition.

Question headings

A question heading turns the reader’s need into the section’s task. With “How is GEO measured?” instead of “Our measurement approach,” the first sentence must explain the method. Under the question comes the direct answer, not a long introduction on why the topic matters.

A sample match: “How is GEO measured?” gets “GEO is measured with a query × engine table that records brand mentions and citations for selected queries across engines.” Query selection, record fields and tracking routine follow. Heading, first sentence and the rest of the section serve the same question.

When drafting questions, we separate definition, difference, implementation and access needs. “What is GEO?” and “What does GEO stand for?” can share a section; “can we block training but allow search?” needs its own technical explanation. That split sets the number of sections.

Sourced data

Sourced data shows a number with what it describes and its basis. Here, the statement “Additional files or special markup required for Google’s AI features: 0” is tied to Google Search Central’s explanation. Its scope is Google’s features, not a technical rule for every tool.

Next to “10 queries” in our method record, we add “per service” and “markention method.” The figure does not describe visibility achieved; it explains how the review is set up. Official product information and our own method are separated in the source column.

If the source states a condition, it stays in the same sentence or table row as the number.

Comparison table

A comparison table shows different answers to the same criterion side by side. The “Measurement” row in the SEO vs GEO table is an example: rankings and clicks for SEO, citations and mentions for GEO. Readers see one question across two areas of work in a single row.

Columns share one level of detail; a goal in one column and a tool name in another breaks the comparison. Cells hold concrete scope, not judgments such as “good” or “advanced.”

The bot table follows the same method: each company gets its search or user bot, then the training bot or control. We do not invent an undocumented bot to fill a cell; the cell text shows the limit of the information.

FAQ

An FAQ answers the short questions readers ask before deciding. For “Are GEO and SEO the same?”, we explain the difference first, then the shared technical foundation. No answer should depend on reading another one.

An FAQ adds no new claims: the special file answer keeps the scope of Google’s explanation, and the timing answer separates observation from tracking method.

Questions are not added to repeat the keyword; the ones here cover the concept, the abbreviation, the difference from SEO, where to start, timing, measurement and special files.

Do you need structured data or a special file?

Google AI Overviews and AI Mode need no new file, special markup or special schema.org data. Google Search Central states that the existing SEO fundamentals apply. If structured data is used, it should match the visible text.

This decides which job comes first: an incomplete definition is clarified, an unindexed page gets an access and index review, and the definition, scope and source must be in the visible text. In the structured data check, we confirm this visible information is carried correctly into the markup; a service missing from the text is never added to the markup alone.

We assess Organization and Article markup as support for the existing content structure, matching the brand and article details in the markup with what the page shows. When a title or service description changes, the markup should carry the same information. Google’s AI features require no extra schema beyond this.

Questions about files such as llms.txt are tied to what each platform states. Google needs no such file, and the official bot documentation of the other platforms covered here does not define llms.txt as a requirement for being cited. We keep the access roles in these documents apart from claims about special files.

If the visible paragraph describes only consulting, the markup does not suggest implementation too. The review delivers a record of which visible information matches which markup and which field needs fixing.

How do we manage AI bot access?

We manage AI bot access by separating search, model training and user-requested visits. Instead of one decision for “all AI bots,” we write down which company’s bot is allowed and for what purpose. The bot’s role sets the scope of the rule.

Google-Extended does not carry exactly the same scope as the other training bots: it covers grounding use along with training, while inclusion and ranking in Google Search are unaffected. So a “we blocked training” note also states which platform’s source use is affected.

OpenAI states the split directly: OAI-SearchBot can be allowed while GPTBot is blocked. Anthropic’s example, “User-agent: ClaudeBot” with “Disallow: /”, shows the rule targets ClaudeBot; we do not automatically extend it to Claude-SearchBot and Claude-User.

To turn the access preference into practice, we take one of these paths:

1. We want to appear in answers but do not allow training → We allow the search bot and set a separate rule for the training bot or control; for Google-Extended, we also weigh the grounding scope.
2. We do not want to appear at all → We review each platform’s search and user bot controls, and Google’s index and preview controls, then write a separate access decision for each platform.
3. Bot access is blocked at the firewall → We check the robots.txt permission and the firewall rule together and identify which bot and which page the blocked request belongs to.

In the second case, “not appearing at all” is not the effect of a single robots.txt line. User-requested visits, crawling for search and indexing are separate areas. Google Search Central describes controls such as nosnippet, data-nosnippet, max-snippet and noindex; limiting the text preview and keeping a page out of the index are separate decisions.

In a firewall review, we start from the page that should be offered as a source: a bot that reaches the home page but is blocked on the service path is still blocked. We check the rule by page and bot and recheck the same request after the change.

The decision record holds the bot name, its role, the page or path, the requested permission and the check finding, so the content and technical teams work on the same scope.

The guide what is robots.txt explains the file’s rule structure, and AI access control covers a site-specific review of bots and access. After the access decision, we return to visibility tracking and record the answer and sources for the query again.

Is my site ready for GEO?

We assess a site’s GEO readiness with eight questions on access, answer structure and source information. Each gets a yes or no, and every no becomes a concrete fix. This list is markention’s working method.

• Is the page indexed? (Search Console › URL Inspection)
• Is there a one-sentence direct answer under each question heading?
• Is the service description the same on the site, the Google Business Profile and LinkedIn?
• Can OAI-SearchBot access the page? (robots.txt)
• Does the page pass Core Web Vitals on mobile? (PageSpeed Insights)
• Do the Organization and Article schemas match the visible content? (Rich Results Test)
• Are the author and date visible?
• Is there a source next to each figure?

The check is not done for the home page only; it is based on the service or guide page we treat as a potential source. If the definition is clear on the home page but vague in the implementation section, the content work starts there. An access problem on one page is not fixed by lengthening another page’s text.

The author, the date and the source next to a figure are what we look at for the experience, expertise, authoritativeness and trust that Google’s E-E-A-T framework addresses; they let readers examine a statement and its basis, and each is checked for its own function.

Organization and Article markup are checked against the visible content; Google’s AI features still need no special schema.

After the check, the order of work is:

1. Every answer is yes → The basic preparation is complete; we move on to measuring the selected queries by engine.
2. Technical questions are answered no → We first remove the access and indexing barrier, then review the content meant to be the source.
3. Content questions are answered no → We fix the definition sentences, question headings, brand information and source attributions.

With several gaps, we start where the page cannot be used. A fix note never says something general like “content will be improved”; it names a task whose result is visible, such as “the query × engine method will be explained in the first paragraph under the measurement heading.” In the final check, we answer the same question again and update the readiness record.

How is GEO measured? For which queries are we cited?

GEO is measured with a query × engine table: selected service queries are tested in different tools, and brand mentions and page citations are recorded in separate fields. Core tracking uses ChatGPT, Gemini and Google AI Overview; when the scope requires, other platforms get their own columns.

These numbers describe how we work; the ten queries cover the service’s definition, selection and delivery. “What is GEO?”, “What is the difference between GEO and SEO?” and “How is GEO measured?” are different information needs; the list is not filled with small rewordings of one question.

Each record holds the query, engine, measurement time, brand mentioned and source page. We also check that the brand is tied to the right service. A brand name without a source link goes into the mention field; a clickable page link goes into the source field. Keeping them apart shows the form visibility took.

The record structure below is a method example and contains no real visibility finding:

If the brand is mentioned but no source link is given, the entry reads “mention, no source” and does not move into the citation column. If a page link is offered without the brand name in the statement, we fill the source field and leave the mention field apart. These are method notes on using the fields, not performance data.

When the source page is correct but the answer stretches its scope, we note both wordings and check whether the condition sits close to the heading. If it is already clear, we log a reporting difference, not a page flaw; not every finding is a reason to rewrite.

The question text stays the same in every measurement; narrowing it or adding the brand name opens a new query row, and follow-up questions are noted separately.

According to Google Search Central, AI Overviews and AI Mode data appears in the Search Console Performance report under the “Web” search type. We do not treat clicks from these features as having their own report: Search Console shows the page’s search performance, while the query × engine table tracks sources and mentions inside answers.

“Query fan-out” matters in interpretation too. Because Google runs several searches on subtopics, we check which subquestion a page cited for a broad question actually answered. If the bot access explanation was the source for a general GEO question, for example, the page’s contribution is recorded through that section, not just by whether its title matches the main query.

Our observation: citations are more volatile than Google rankings; they change within a week for the same query. That is why we track for four weeks, keep each answer’s record and compare it with the next review. Where we do not appear, we look at which page was chosen instead, how it answers the question and how accessible our own page is.

We also review a site’s readiness with the AI visibility tool, which takes the site address and scores access, structured data and content signals against a checklist. This readiness check is not a record of being cited in a real answer; query × engine tracking is where answers are reviewed.

How does GEO work at markention?

GEO work at markention moves through building the query list for a service, setting up the query × engine table, making content and access fixes, and measuring the same queries again. Each step’s output shows on which page and for what reason the next job is done.

We tie the query list to the service scope

First, we identify the service the business actually offers, its audience and the conditions it must explain. Definition, comparison, selection and implementation questions come from this scope. A service the business does not offer is not added just because a related query exists.

Questions with the business’s own name are grouped apart from category questions. “How does GEO work at markention?” is a brand and process question, assessed against the service scope; “how do you do GEO?” asks how the concept is applied, assessed against the method explanation.

Next to each query, we write the current page and the section where the answer should be. A missing section and an access problem are logged as separate jobs, so the content team sees which explanation to complete.

We create the first measurement and the fix log

We run the selected queries in the chosen engines and fill in the mention, source and reported information fields. This baseline shows what was observed before any edits. Then we match the definition sentence, sourced data, brand consistency and bot access checks to the relevant page.

If the section cited for a measurement question holds only a general introduction, we add the method explanation. If OAI-SearchBot access is closed, a search access job opens alongside the content edit. The change log records the page, the point fixed and the reason; issues are not bundled under a vague “GEO improvement” heading.

We measure again with the same questions

After the fixes, we return to the same query list and record the engines’ answers again. The brand description, source page and related explanation are compared between the earlier and later records, and each cell is assessed in its own context rather than one change being read across all queries.

GEO consulting is the service where we run content and access work with this tracking routine. Within AI visibility measurement, query and source records are read separately to set the next review’s priority: which question was tested, what changed and what the re-check showed.

GEO is where SEO extends into AI answers. An accessible page, a clear definition, consistent brand information and sourced content are handled together, and visibility is tracked query by query. Our GEO articles build on the same ground to look at other applications of this method.

Frequently asked questions