Generative Engine Optimization (GEO): What It Is, Where It Came From, and How to Do It

Generative engine optimization (GEO) is the practice of shaping web content so that generative engines, AI systems that search the web and write a synthesized answer, include and cite your pages and your brand in what they generate. It applies to ChatGPT, Google AI Overviews, Perplexity and Gemini, builds on SEO, and overlaps heavily with answer engine optimization.

If you are here for the acronym soup (SEO, AEO, GEO, AIO), the short version is that they describe one goal with different emphasis, and we cover that in AEO vs GEO. This post is about GEO on its own: the definition, the research, and the work.

What Is a Generative Engine?

A generative engine is a search system that answers with generated text instead of a ranked list. It takes a question, retrieves a handful of pages from a web index, reads the relevant passages, and writes an answer that blends them, usually with citations back to the pages it used. ChatGPT with search on, Google AI Overviews and AI Mode, Perplexity, Gemini and Claude with web access all work this way.

The word "generative" is the important one. A classic engine ranks documents. A generative engine composes an answer from several documents at once, which means the competition is not for a position but for inclusion. Your page either contributes a sentence to the answer, with a citation, or it contributes nothing. GEO is the work of being one of the contributors.

Where the Term Came From

The phrase was coined in a paper titled "GEO: Generative Engine Optimization", posted to arXiv in November 2023 by researchers at Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi, and later published at KDD 2024. The paper gave it a name, a benchmark and a set of measured results, which is why GEO stuck as the academic term while AEO became the more common one in marketing.

The authors built GEO-bench, a set of 10,000 queries drawn from multiple sources and domains, and tested nine content changes against a generative engine built on a commercial LLM plus a live search backend, then repeated part of the test on Perplexity. They measured visibility two ways: how much of the answer came from the page and how prominently (they call this position-adjusted word count), and a judged impression score for how well the page's content was represented.

What the Paper Found

Three changes to a page's content stood out: citing sources, adding statistics, and adding quotations. Across the benchmark, the best of these boosted a page's visibility in generative engine responses by up to 40 percent against the unoptimized baseline (41 percent on the word-count metric, 29 percent on the impression metric). Keyword stuffing, the tactic closest to old-school SEO, did not help, and in the Perplexity test it scored 10 percent below doing nothing.

The finding that matters most for small teams is buried in a later table. When the authors optimized every source at once, pages that ranked lower in classic search gained far more than pages already at the top. In their example, citing sources raised visibility for the page ranked fifth by 115 percent while the top-ranked page lost visibility. The paper reads that as generative engines leveling the field: a well-evidenced page from a small site can be cited ahead of a vaguer page from a large one.

Two honest caveats. The study is from late 2023, the engines have changed a great deal since, and the authors themselves note that results vary by domain. Treat the numbers as a direction, not a forecast.

GEO vs SEO

GEO does not stand in for SEO. Every generative engine retrieves from an index built by classic crawling and ranking (Google's for AI Overviews and Gemini, Bing's plus OpenAI's own crawler for ChatGPT search), so a page with no search presence has nothing to be cited from. The base layer is unchanged: crawlable pages, fast loads, clear structure, links from sites the engines trust. Our AI SEO page covers how that base changes when AI answers are the target.

What changes is the unit and the outcome. SEO competes at the page level for a position. GEO competes at the passage level for inclusion, and the outcome is often a mention rather than a click. A page can rank third and never be cited, while a shorter page with a clean, cited, specific paragraph gets quoted. That is why GEO work looks like editing rather than link building, and why the paper's tactics are all content edits.

GEO vs AEO

Answer engine optimization (AEO) is the practice of structuring content so that answer engines can extract it and present it as the direct answer to a question. GEO is the practice of shaping content so that generative engines include and cite it when they synthesize an answer from many sources. The first is about being the answer; the second is about being in the answer. In day-to-day work the two produce the same to-do list, which is why Mavek treats them as one discipline. The full breakdown, including where AIO (Google AI Overviews) fits, is in AEO vs GEO, and the tactical side is in the answer engine optimization guide.

The GEO Checklist

Seven steps, in order. Steps one to three are free and take a day. Steps four to seven are the ongoing work.

• Pick the prompts, not the keywords. List the 30 to 50 questions a buyer in your category asks a generative engine. These are longer and more specific than search keywords. 'Best CRM for a 5-person team in Dubai' rather than 'best crm'.
• Run them and record the baseline. Ask each prompt in ChatGPT, Google AI Overviews, Perplexity and Gemini. Log whether you are mentioned, which page is cited, and who else appears. This is your before picture.
• Fix retrieval first. If your pages are not in Google and Bing, nothing else matters. Allow GPTBot, ClaudeBot and PerplexityBot in robots.txt, verify the site in Bing Webmaster Tools, and turn on IndexNow.
• Rewrite the pages the engines already fetch. For each page that appears in your baseline, or should, add a direct-answer paragraph under every H2, cite sources, add specific numbers, and quote a named expert or your own data. This is the paper's finding applied to one page at a time.
• Publish original evidence. Engines cite primary sources first. A survey, a benchmark, a price table you assembled, a case study with real numbers. One primary page beats ten summaries.
• Earn mentions on the sources engines trust. Generative engines lean on third-party consensus more than on your own site. Comparison posts, review sites, community threads and industry publications shape what the engine says about you.
• Re-run monthly. Repeat the prompt panel, compare share of mentions and citations to the baseline, and route the losses back to step four.

Step four is where the paper's findings turn into edits. Take a page, find each claim that has no support, and do one of three things: link to the source it came from, swap the adjective for a number, or quote the person or dataset that backs it. Then check the first paragraph under every H2 can stand alone as a 40 to 60 word answer. That pass, on the twenty pages that matter, is most of a GEO program. Our FAQ schema generator covers the markup side and the IndexNow guide covers getting into Bing.

How to Measure It

GEO cannot be measured from analytics alone, because inclusion in an answer often sends no click. The working method is the prompt panel from step two, run on a schedule, with three numbers per prompt and engine: mentioned or not, cited or not, and who else appeared. Your share of mentions across the panel is the headline metric, and the competitor list is where the strategy comes from.

Google Search Console folds AI Overview and AI Mode impressions into its normal Search performance report without breaking them out, so the Google side is partly visible for free. Referrals from chatgpt.com and perplexity.ai show up as their own sources in GA4. Mavek's AI visibility tracker runs the prompt panel for you across ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews, per market, and logs mentions, citations and competitors over time. For the wider market of tools that do this, see the best AI visibility tools.

What GEO Does Not Do

It does not create demand. Generative engines answer questions people are already asking; if nobody asks about your category, there is no answer to appear in. It does not produce quick traffic, since citations arrive over weeks and the click volume from AI engines is still small next to Google. It does not rescue thin content, because the tactics that worked in the paper (citations, statistics, quotations) all require having something real to cite. And it does not need a separate department. It is an editing discipline run against the same pages your SEO already targets. If you want that work done for you alongside the SEO underneath it, that is the scope of our SEO and AEO service. If you would rather run it in-house, the SEO agent handles crawls, briefs and drafts inside the platform, and SEO for SaaS is the companion read for software companies.

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