Generative engine optimization (GEO) is the work of getting your company named and cited in the answers AI tools write, in ChatGPT, Perplexity, Gemini and Google’s AI Overviews. The term comes from a 2023 research paper whose best methods raised a page’s visibility in AI-written answers by up to 40% in its tests.
For B2B companies the reason to care is where buyers start. In G2’s March 2026 survey of 1,076 B2B software buyers, 51% began their research in an AI chatbot more often than in Google. 69% chose a different vendor than they first planned because of what a chatbot told them.
What does GEO stand for?
GEO stands for generative engine optimization. The paper that named it defines a generative engine as a search system that finds information and writes a response drawn from several sources. Its examples were Bing Chat, Google’s Search Generative Experience and Perplexity. By that definition, ChatGPT, Gemini, Claude, Copilot and Google’s AI Overviews and AI Mode are generative engines too.
In marketing, geo also has a geographic sense, as in geotargeting: showing different ads or content to people based on where they are. Profound, which sells AI visibility tracking, prefers the term AEO partly because GEO collides with geography and geo-targeting. Google’s own guide spells GEO out as generative engine optimization.
Where did the term come from?
GEO: Generative Engine Optimization was posted to arXiv on 16 November 2023 by six researchers, led by Pranjal Aggarwal of IIT Delhi and Vishvak Murahari of Princeton. It was accepted at KDD 2024, the ACM’s conference on knowledge discovery and data mining.
The authors built a benchmark they called GEO-bench, with 10,000 queries from nine sources. They range from real Bing and Google searches to questions from Perplexity’s Discover page and Reddit’s ELI5 forum, and the methods were scored on its 1,000-query test split. For each query they took the top five Google results and had GPT-3.5 Turbo write an answer from them. Then they rewrote one source with each method and measured how much of the answer drew on it.
What did the GEO research find?
The methods that added evidence did best, and keyword stuffing, the classic SEO move, did little or nothing:
| Method | What it changes on the page | Result in the paper |
|---|---|---|
| Cite Sources | Adds citations to credible sources | One of the three best; 115.1% more visibility for a source ranked fifth in Google |
| Quotation Addition | Adds quotations from credible sources | One of the three best; the best on Perplexity by the word-count measure, at 22% |
| Statistics Addition | Swaps qualitative claims for numbers | One of the three best; with fluency edits, the strongest pair of methods |
| Keyword Stuffing | Adds more of the query’s keywords | Little or no gain; 10% below the unchanged page on Perplexity |
The three best methods raised a source’s visibility by 30% to 40% on the paper’s word-count measure. That measure counts how much of the answer comes from the source, weighted by how high it appears. On a second, more subjective measure, the gain was 15% to 30%. Statistics Addition paired with fluency edits beat any single method by more than 5.5%.
Lower-ranked pages gained the most. The authors also rewrote every source at once and broke the results down by Google rank. Adding citations raised visibility by 115.1% for the source ranked fifth and lowered it by 30.3% for the source ranked first. The authors read this as a chance for smaller sites, because a generative engine writes from the content of the pages it fetched.
Read the percentages with the setup in mind: the main test had GPT-3.5 Turbo answer from five Google results. The Perplexity test, on 200 queries, uploaded the sources as files, so it measured how Perplexity wrote from them and left its own search out. Today’s assistants pick their own sources, and Google’s can run several related searches per question. Treat the figures as the direction of an effect, and test on your own questions.
How does generative engine optimization work?
An AI answer is built in two steps: the system retrieves pages, then a model writes from them. Google’s guide to its generative AI features calls the first step grounding, or retrieval-augmented generation. Its AI features use Google’s core ranking systems to fetch pages from the Search index, then write a response with links to the pages that support it.
Google adds a second technique, query fan-out: the model runs a set of related searches at the same time to gather more results. In Google’s example, a question about a lawn full of weeds fans out to searches on herbicides, chemical-free removal and prevention.
Fan-out means a cited page does not have to rank for the question the buyer typed. In Ahrefs’ study of 4 million URLs cited in AI Overviews, updated March 2026, about 38% also appeared in the top ten results for the same query. In July 2025 the share was about 76%. Ahrefs suspects fan-out is part of the reason, and it also changed how it parses citations between the two studies.
ChatGPT and Perplexity run their own search crawlers. OpenAI says sites that block OAI-SearchBot will not be shown in ChatGPT search answers. Perplexity recommends allowing PerplexityBot in robots.txt so a site can appear in its results.
So GEO works on four things. Each system has to be able to reach and index the page. The page has to answer the questions a fan-out produces. Its claims have to be specific enough to quote, and it helps when other sites the system trusts mention you.
What is the difference between GEO and SEO?
SEO wins a ranked link on a results page. GEO wins a mention or a citation inside a written answer. The base is shared. Google published its guide to generative AI features in May 2026 and last updated it in July. The guide says that for Google Search, optimizing for generative AI “is optimizing for the search experience, and thus still SEO.”
| SEO | GEO | |
|---|---|---|
| What you win | A ranked link on a results page | A mention or citation inside the answer |
| Where it shows | Google and Bing results | ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews and AI Mode |
| How the result is chosen | Ranking systems order the pages | Retrieval, which can add fan-out searches, then a model writes from the pages |
| How you measure it | Rankings, clicks and Search Console’s Performance report | How often a fixed list of buyer questions returns your name, plus Search Console’s Generative AI report |
Keyword stuffing, the one classic SEO tactic the paper tested, made little or no difference there. For the full comparison, with measurement and budget, see our GEO vs SEO page.
Is GEO the same as AEO?
Mostly, yes. AEO, answer engine optimization, is the older term. Search Engine Watch wrote about it in February 2018, when voice search and featured snippets were turning search engines into answer engines. GEO arrived in 2023 with generative AI. Wikipedia’s entry notes there is no consensus definition, and lists AEO, LLMO (large language model optimization), AIO and AI SEO as other names for the same practice. Google’s guide treats AEO and GEO together, as work it still calls SEO.
The cleanest way to separate them is by origin. AEO grew out of featured snippets and voice assistants that read out one answer to one question. GEO comes from a paper about answers an AI writes from several sources, such as a shortlist of vendors. Our pages on AEO and AI search optimization show how the three terms divide the work, and the glossary entry on AEO gives the short version.
What does Google say you can skip?
Google’s guide lists things it says you can ignore for Google Search, its AI features included:
- llms.txt files. Google Search does not use them, so one neither helps nor hurts there. Google says keeping one for other services that use such files is fine.
- Chunking. There is no need to cut content into small pieces for AI to understand it.
- Rewriting for AI. No special writing style is needed, because its systems understand synonyms and general meaning.
- Inauthentic mentions. Chasing them helps less than it looks, since Google’s ranking systems favor high-quality content and other systems block spam.
- Structured data. It is not required for AI features, and no special schema exists for them, though it still helps with rich results.
Google also warns against volume for its own sake. Creating separate content for every way people phrase a search, fan-out queries included, mainly to manipulate rankings or AI answers, breaks its scaled content abuse policy. A high count of pages, Google says, does not make a site higher quality.
Two other parts of the guide matter for planning. Google asks for non-commodity content: a first-hand view or an expert’s experience, which a summary of other pages cannot supply. And it now reports on AI visibility directly, in a Generative AI performance report in Search Console.
How do you do generative engine optimization?
For a B2B software company, the work runs in this order:
- Let the crawlers in. Allow Googlebot, OAI-SearchBot and PerplexityBot in robots.txt and at your CDN, and check that your important pages are indexed.
- Write down the questions buyers ask. Use their words: “best payroll software for a 50-person company”, where a keyword list would say “payroll software”.
- Ask them in the assistants your buyers use. Record which vendors each answer names, and what it says about you.
- Publish a page for each question you lose. Build it from what your experts know, with numbers and their sources. That is what Google calls non-commodity content, and it carries the citations, quotations and statistics that raised visibility most in the paper.
- Earn real mentions elsewhere. Omniscient Digital analyzed more than 23,000 AI citations. When a user asked about a brand by name, owned content made up 23% of what was cited, and earned media such as reviews, coverage, forums and comparisons nearly half. In G2’s survey, 45% of buyers named citations from software review sites as the signal in an AI answer that gave them the most confidence.
- Ask the same questions every month. Answers vary between runs, so read the trend across months more than any single reply.
Do you need extra budget for GEO?
Not necessarily as a separate line. Google’s AI answers draw on the same Search index that SEO works on, and the other assistants crawl the same public web. GEO changes what an SEO budget buys: pages built around buyer questions with sourced facts, plus a monthly check of the answers.
If you buy it as a service, the published prices we read on 4 November 2026 started at $3,000 a month. That was the entry price at WebFX, with a 6-month commitment, and the starting price of Optimist’s ongoing AEO advising.
Growigami’s search process is $1,999 a month for 200 pages, with a minimum of three months. A process costs $500 more in its first month while we build it, and your first build is on us this month. AI agents write the pages from a call with your expert and check every figure against its source. A person on your team approves each batch before it goes live. Google’s scaled content policy targets pages made mainly to manipulate rankings or AI answers. Each of our pages starts from a question your buyers ask and from what your expert said. The generative engine optimization page covers what the process includes and what it costs.
Frequently asked
What is GEO in simple terms?
GEO is the work of getting your company into the answers AI tools write, with the facts about it right. When a buyer asks ChatGPT which software to pick, the answer names a few vendors and cites a few pages. GEO covers the pages, the mentions on other sites and the crawler access that affect whether yours are among them.
Is GEO replacing SEO?
No, and SEO is not dead either. Google says its AI features rest on its core Search ranking systems. In Ahrefs' March 2026 data, about 38% of URLs cited in AI Overviews also appeared in the top ten results for the query, down from about 76% in July 2025. Ranking counts for less than it did, and it still feeds citations. What changes is the scoreboard: you also track whether the answers name you.
What is the best tool for generative engine optimization?
Start with the free ones: the Generative AI performance report in Google Search Console, plus a fixed list of buyer questions you run by hand each month. Paid trackers such as Semrush's AI Visibility Toolkit, Profound and Peec run those questions across assistants for you. Google notes that no third-party tool has access to its internal ranking or AI systems.
Does llms.txt help with GEO?
Not on Google. Google's 2026 guide to its generative AI features says Google Search ignores llms.txt files, so one neither helps nor hurts there. Google also says keeping one is fine for other services that use such files. Put the effort into the pages and the facts on them first.
How long does GEO take to work?
A new page has to be crawled and indexed before Google's AI features can use it. Google says recrawling a page to pick up a change can take from several days to several months. After that, answers vary from run to run and shift with model updates. Judge GEO over months, on the same list of questions each time.
Can anyone guarantee that ChatGPT will cite my company?
No. Each assistant picks its sources question by question, and answers change with wording and with model updates. Google says indexing and serving are not guaranteed even for pages that meet every requirement. Judge a provider on the work it delivers and on a monthly record of which answers name you.
Who coined the term generative engine optimization?
Six researchers led by Pranjal Aggarwal of IIT Delhi and Vishvak Murahari of Princeton. Their paper, GEO: Generative Engine Optimization, was posted to arXiv in November 2023 and accepted at KDD 2024, the ACM's conference on knowledge discovery and data mining.
