Generative engine optimization: what GEO is and how it works

What is generative engine optimization?

A generative engine is any system that writes its own reply to a question — a chat assistant, an AI search engine or an AI summary above Google results. GEO is everything you do so that reply includes your brand, describes it correctly and sends people to you. It is one part of AI visibility, the broader question of how often and how well AI represents a brand.

In practice, GEO comes down to three questions. Does the answer name you at all? Does it recommend you, or only list you among others? And does it link to a page you control, or to someone else's page about you?

Where the term comes from

The name comes from academic research, not from a vendor. Pranjal Aggarwal and five co-authors published GEO: Generative Engine Optimization in November 2023, and the paper was presented at KDD 2024. They built a benchmark of user queries across many domains, together with the web sources that answer them, and measured how changes to those sources affected their visibility in generated answers.

Two findings are worth keeping. In their tests, GEO methods raised visibility in generative engine responses by up to 40%. And what worked varied by domain — there was no single trick that helped everywhere.

Since then, marketers have stretched the term well beyond the paper. There is still no agreed definition, and GEO, AEO, LLM SEO and AI SEO are often used for the same work.

How does generative engine optimization work?

An AI answer about your category draws on two things. The first is what the model learned in training — its memory of how the web described brands and products up to a cutoff date. The second is what the engine looks up at the moment of the question, when it searches the web and grounds its answer in the pages it finds.

The two paths change at very different speeds. Memory updates only when a new model version ships, so it rewards brands that have been described consistently, in many places, for a long time. Live retrieval can change once a page is crawled and indexed, so it rewards pages that answer the exact sub-question the engine looks up.

Google describes the retrieval step in its guide to AI features in Search. AI Overviews and AI Mode may issue multiple related searches across subtopics — a technique Google calls query fan-out — before they write a response. One question can therefore pull in pages that rank for questions the user never typed. For Google's surfaces in particular, see how to rank in AI Overviews.

What GEO work actually involves

Most GEO work is familiar marketing and SEO work aimed at a new target. What changes is the test you apply to every page: could an engine that reads it name you, describe you and say who you are for?

• Answer category questions on your own site. Write pages for the questions buyers ask before they know your name — “best X for Y”, “X vs Y”, “how much does X cost”.
• State product facts in plain text. Who it is for, what it does, what it does not do, where it is sold. Engines cannot quote what sits only in an image or behind a script.
• Earn mentions on pages engines already use. Reviews, editorial roundups, comparison sites, retailers and community threads can shape an answer as much as your homepage.
• Keep facts consistent everywhere. When your site, your listings and third-party pages disagree, the engine has to guess which version is right.
• Keep the technical door open. AI search crawlers must be able to reach and read your pages — the LLM SEO checklist lists the user agents to check.
• Use software where it saves time. If you want help with content and tracking, compare AEO tools — the same category covers GEO tools.

GEO vs SEO: what carries over and what changes

Classic SEO still does a lot of the work. Google says there are no additional requirements or special optimizations needed to appear in AI Overviews or AI Mode — a page must be indexed and eligible to show a snippet. If search engines cannot find or read your pages, generative engines that search the web will struggle too.

What changes is what success looks like. A ranking is one position for one query. An AI answer is a paragraph that may name five brands in a different order each time, differ from engine to engine and never send a click. That is why GEO is judged by your share of answers, not by a single position.

The same split applies to answer engine optimization. The practical differences are laid out in AEO vs SEO.

Is AI search optimization the same as GEO?

Mostly, yes. AI search optimization is the plain-English name for the same goal — getting your brand into answers written by AI search tools. Some people use it narrowly for engines that search the web live, such as Perplexity, ChatGPT search and Google AI Overviews. They keep GEO for every generative system, including assistants that answer from memory.

One more trap. “GEO optimization” in this sense has nothing to do with geographic targeting or local SEO. If a guide talks about map packs and city pages, it is about a different GEO.

The names vary with who is writing. If you are wondering what AI search optimization is called in guides and job ads, expect at least five:

• Generative engine optimization (GEO) — the research term and the most common one in guides.
• Answer engine optimization (AEO) — older, rooted in featured snippets and voice answers.
• LLM SEO or LLM optimization (LLMO) — used by teams who see it as SEO for large language models.
• AI SEO — ambiguous, because it also means doing SEO with AI writing tools.
• AI search optimization — the descriptive name most people type first.

GEO, AEO and LLM SEO: same goal, three names

The three terms overlap far more than vendors suggest. Answer engine optimization grew out of featured snippets and voice search, so it stresses one clear, extractable answer. LLM SEO is the hands-on version: crawler access, content and the habits of ChatGPT, Gemini and Perplexity.

If you must pick one word for a plan or a job title, the choice matters less than the measurement behind it. For a side-by-side look at where the terms really diverge, read AEO vs GEO.

How to measure generative engine optimization

GEO has a measurement problem: the data you already have rarely answers the question. Google's generative AI performance report in Search Console, open to all sites since 31 August 2026, counts how often links to your pages were shown in AI Overviews and AI Mode — by page, country, device and date, with no queries. Bing's AI Performance report, in public preview since February 2026, counts how often your pages are cited in Microsoft Copilot and Bing's AI summaries.

Both count links to your pages. Neither tells you whether ChatGPT, Gemini or Perplexity recommend you, or which competitors they name instead — for that, you have to ask the engines. Pick the questions your buyers ask, run each one more than once on each engine and count the answers.

The free AI visibility checker runs that check for up to 20 questions on ChatGPT, Gemini and Perplexity and sends one report by e-mail. It shows your mention rate, recommendation rate, average position, citation rate and AI Visibility score, plus the competitors that appear instead. The AI visibility guide explains each number, and if you need the same check every month, see how ongoing AI visibility tools compare.