What is GEO (generative engine optimization)?

Generative engine optimization (GEO) is the practice of making content and brands visible in answers produced by generative engines: search tools that use a large language model to read several sources and write one answer from them. ChatGPT with search, Claude with web search, Perplexity and Google's AI Overviews and AI Mode all work this way. Where a classic search engine returns ranked links, a generative engine returns a paragraph, a list or a table, often with a few cited sources underneath. GEO asks what makes your content one of those sources, and your brand one of the names in the answer.

Where the term comes from

The term was introduced in a 2023 research paper, GEO: Generative Engine Optimization, by Pranjal Aggarwal and colleagues, later accepted at the KDD 2024 conference. The authors described generative engines as systems that gather information from multiple sources and summarize it, pointed out that website owners have little control over when and how their content appears in those summaries, and proposed a framework and a benchmark of queries (GEO-bench) for measuring visibility in generative answers.

Two points from that paper are worth keeping in mind. They reported that some changes to content made it more visible in generative answers in their test setup, and that what worked varied from one domain to another. In other words, even under controlled conditions there was no single recipe. Since then the term has spread well beyond research, and today it is used loosely, often interchangeably with answer engine optimization.

How GEO differs from SEO

Search engine optimization is about ranking a page in a list of results. GEO is about being used and named when an AI writes the result itself. The two overlap heavily, since a generative engine that searches the web usually starts from pages a search engine can find, but the goal and the evidence are different.

• Synthesis instead of ranking. A generative engine combines several sources into one answer. Your page can be read and still leave no trace in the answer, or your brand can be named because of someone else's page.
• Visibility is shared. An answer has room for a few names. Being one of them depends on how you compare with the alternatives in the sources the engine reads, not only on the quality of your own page.
• Accuracy matters as much as presence. A generative answer describes your brand in its own words. GEO includes making sure there is clear, current information available, so that when you are named, the description is right.
• There is little first-party data. Search engines report impressions and clicks to site owners. Generative engines don't report when they mentioned you, so GEO has to be measured from the outside.

What seems to influence whether a model names a brand

The internals of commercial generative engines aren't public and change often. What can be observed, by asking the same questions many times and reading the answers and their sources, points to a few recurring factors. They are observations, not a proven algorithm.

• Presence in the sources the engine cites. When an answer lists its sources, those pages are the most concrete lever available. If the pages an engine relies on for a question don't mention you, the answer usually doesn't either.
• Comparison and alternatives content. Questions like “best tools for…”, “X or Y?” and “alternatives to X” are where buyers meet brands they don't know yet. Honest comparison pages, on your site and on independent sites, give an engine material that covers you and your competitors together.
• Clear, specific, factual text. Engines have to extract statements they can repeat: what a product does, who it is for, where it is sold, what it costs. Content that states these plainly, in the page's text rather than in images or client-side scripts, is easier to use than vague positioning.
• Crawlers that can reach your pages. The companies behind these engines document their crawlers, such as OAI-SearchBot, Claude-SearchBot, PerplexityBot and Googlebot. Blocking search crawlers in robots.txt, or serving pages that show nothing without JavaScript, limits what those engines can read.

What nobody can promise

GEO has real limits, and honest work in this area starts by naming them.

• Answers vary between runs. The same question, asked twice, can produce different brands in a different order. Only repeated measurement tells you whether a brand is visible or just got lucky once.
• Models change without notice. Engines update their models, their search providers and their answer formats. Visibility can move for reasons outside anyone's control.
• Effects take weeks. New or updated content has to be crawled and picked up before it can show in answers, and third-party coverage takes longer to earn. Expect to judge changes over weeks, not days.

Nobody can guarantee that a generative engine will name a particular brand. What can be done is to improve the odds and measure whether they improved.

How you would measure it

Measuring GEO means building a fixed set of the questions your buyers ask, asking them regularly to the engines they use, and recording which brands each answer names, in what order and on the basis of which sources. The share of answers that mention you is your visibility; the sources cited where you were missing show where to work. Because answers vary, the trend over several scans matters more than any single result.

Prolpy is built to do exactly this across ChatGPT, Claude and Google AI Mode, for your brand and your competitors, with the cited sources and a list of what to improve. The pricing page explains how answers are counted.

GEO and AEO

Answer engine optimization (AEO) is the older term for much the same goal, rooted in featured snippets and voice assistants rather than research on generative search. In practice the advice for both is the same. For that angle, read what AEO means.