What is generative engine optimization (GEO)?
Generative engine optimization (GEO) is the work of improving how generative AI engines describe and recommend your brand, so you are named and cited in their answers. GEO and answer engine optimization (AEO) are two names for the same work. GEO is the name used in research; AEO is more common in marketing. Neither is a separate discipline from the other, and neither replaces SEO: engines that search the web still read pages that search engines can find.
Where does the term GEO come from?
The term comes from a 2023 research paper, GEO: Generative Engine Optimization by Aggarwal and colleagues. It framed generative engines as systems that retrieve sources and write one answer from them, and asked what a content creator can change to be more visible in those answers.
The paper tested rewriting methods across a benchmark of queries and reported that some, such as adding citations, quotations and statistics, made a source noticeably more visible in generated answers, with gains of up to 40% on its own visibility measures. Its main lesson for practitioners was less about any one method and more about the shift: visibility is decided inside an answer, not on a list of links.
How is GEO measured?
GEO is measured by sampling: ask the engines your buyer questions repeatedly, and track how often the answers name you, how they describe you, and which pages they cite.
- AI visibility, as a mention rate per engine with an interval.
- Share of voice against the brands named instead.
- Citation share: how many of the engines' sources are your pages.
- Accuracy: whether answers to questions that name you describe you correctly.
What does GEO look like in a real example?
The public Tesla example (4 Oct 2026) shows why GEO is measured per engine. Across the same 25 buyer questions, ChatGPT and Google AI Overviews each named Tesla in 16 answers, Claude in 11 and Perplexity in 10. On the five solar panel installation questions, Google AI Overviews named Tesla in 4 and Claude in none.
It also shows how much of the work is off-site. Across those answers, fewer than 2 in every 100 citations went to tesla.com, and video, reference and forum sites were cited more often. Even a famous name can be read the wrong way: Google AI Overviews answered "What is Tesla?" by opening with the magnetic unit of the same name. GEO is as much about the pages that describe you as about your own.
Which tool is better for enterprise GEO and AEO strategies?
The right tool measures the answers rather than estimating them, on every engine your buyers use, with enough repetition to trust a change. Ask any vendor:
- Which engines are asked, and are they asked the same questions in the same words?
- How many answers is each figure based on, and is there an interval beside it?
- Are questions that name you kept separate from ones that do not?
- Can you see the full answer text and every cited page behind a number?
- Does it say what to fix, or only report the score?
Where does GEO fit in Proofsource?
Proofsource measures GEO the way this page describes: your buyer questions on ChatGPT, Perplexity, Claude and Google AI Overviews, mention rates with 95% intervals, who is named instead, the pages cited, and what to fix. See what a GEO tool does and the AI Visibility feature page.
Common questions about Generative engine optimization (GEO)
Is GEO the same as AEO?
Yes, in practice. Both mean getting your brand named and your pages cited in AI answers. GEO comes from a 2023 research paper; AEO is more common in marketing. Some people use GEO for generative engines and AEO for any engine that answers directly, but the work is the same.
Does GEO replace SEO?
No. Engines that search the web, including Google AI Overviews and Perplexity, read pages that are indexed and readable. GEO adds a second test on top of SEO: whether the answer built from those pages names you.
How long does GEO take to show results?
It depends on the engine. Engines that search the web for each answer can pick up a new or changed page as soon as it is crawled. Answers drawn from model knowledge change only when the model is retrained. Re-measure on the same questions after every change and judge it against the interval.
What metrics best measure AI recommendation performance?
Mention rate per engine with an interval, position when named, share of voice against named competitors, citation share, and the accuracy of answers that name you. Read them together; any one on its own can mislead.
Related terms
- Answer engine optimization (AEO)
Answer engine optimization (AEO) is the work of getting your brand named, and your pages cited, in the answers AI assistants give.
- AI visibility
AI visibility is how often AI assistants such as ChatGPT, Perplexity, Claude and Google AI Overviews name your brand when people ask about your category.
- Citation share
Citation share is the share of all the citations in AI answers that point at a given website.
- Google AI Overviews
Google AI Overviews are the AI-written summaries Google shows above some search results, with links to the pages they draw on.
Worked example: Tesla, public AI answers, 4 Oct 2026: Proofsource's first scan of Tesla, 25 buyer questions and 5 brand questions, one answer per question on each of four engines. A small sample from one brand, shown as an example, not a benchmark.
See where AI leaves you out, and what to fix.
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