What Is Generative Engine Optimization (GEO)? A Practical Definition for Marketers

Generative Engine Optimization is a specific, named academic discipline, not marketing shorthand invented after ChatGPT got popular. It was coined in a November 2023 paper by researchers at Princeton, Georgia Tech, the Allen Institute for AI and IIT Delhi, later presented at the ACM SIGKDD 2024 conference. That matters, because most articles using the term never mention where it came from or what the original research actually tested, and the paper’s own findings come with a caveat that a lot of GEO advice quietly skips.

This article answers what is GEO with a practical, sourced definition: where the term originated, what the founding research found (including its limits), how GEO differs from SEO and AEO, and what marketers can do about it today. The short version: GEO means optimizing content to be cited inside an AI-generated answer rather than clicked from a list of links, and the responsible version of it never means inventing statistics or quotes to game a model.

What Is GEO? A Working Definition

Generative Engine Optimization (GEO) is the practice of optimizing content so it is more likely to be retrieved, synthesized and cited by AI systems that generate answers from multiple sources, such as ChatGPT with search, Perplexity, Google’s AI Overviews and AI Mode, and Microsoft Copilot. Where SEO aims for a ranking position on a results page, GEO aims for a citation inside a generated paragraph.

The term comes from a specific paper: “GEO: Generative Engine Optimization” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, posted to arXiv in November 2023 and later published at KDD ’24. The authors describe generative engines as a new kind of search tool that “generate accurate and personalized responses” by synthesizing multiple sources with an LLM, and note this creates a problem for the “third stakeholder” in search: content creators, who have little visibility into when or how their content gets used.

Where Did the Term GEO Come From?

The Princeton-led team built GEO-BENCH, a benchmark of 10,000 queries across nine domains, to measure how visible different websites are inside a generative engine’s response. They then tested nine content-modification strategies against that benchmark and measured the change in two metrics: how much of the generated answer a source contributed, and how prominently the engine credited that source.

This detail matters: GEO is a specific, falsifiable piece of research with a named benchmark, not an industry buzzword with no origin. Wikipedia and most serious industry coverage treat this paper as the founding reference for the term.

What Did the Original GEO Research Actually Find?

The founding GEO study found that adding statistics, quotations and source citations to content produced the largest visibility gains, in the range of 30 to 41% relative improvement, while stylistic changes like clearer writing produced smaller but still meaningful gains of 15 to 30%. Keyword stuffing produced almost no effect.

Figures are drawn from the paper’s reported results and vary slightly across metrics; treat the ranges as directional, not exact percentages you’ll replicate on your own content. The strongest combination the authors found was pairing Fluency Optimization with Statistics Addition, which outperformed any single strategy on its own.

One finding matters more than the headline numbers for smaller and mid-sized sites: sources ranked around position 5 saw visibility improve by roughly 115% when optimized, while the position-1 source in the same set sometimes lost visibility. In other words, generative engines don’t automatically hand the answer to whoever already ranks first, which is a genuinely different dynamic from traditional search.

An Important Caveat the Research Itself Flags

Here is the part most GEO explainers leave out. To test the Statistics Addition and Quotation Addition strategies, the researchers instructed their content-generation prompts to add supporting data and quotes, and independent analysis of the paper’s prompts found language instructing the model that inventing sources and fake data was expected for the experiment. The paper itself, on page 5, classifies its nine strategies into two categories: Content Addition and Stylistic Optimization, and it’s the Content Addition methods, tested with permission to fabricate, that produced the largest gains.

That does not mean the underlying mechanism is fake. The paper’s authors and later researchers reasonably conclude that generative engines reward content offering something concrete and attributable: a specific number, a named source, an exact quote, because that gives the model a discrete unit it can safely include. But it does mean the practical, responsible takeaway is not “add any statistic or quote.” It’s: include statistics and quotes that are genuinely true and attributable, because a model has no way to tell your real citation from a fabricated one, and a fabricated one that gets cited becomes your brand’s misinformation problem when someone checks it.

GEO vs SEO vs AEO: What’s the Difference?

These three terms overlap heavily in practice, and the industry often uses them loosely, but the target surface differs.

In practice the tactics overlap substantially: a page that’s crawlable, well-structured, answers a question directly and cites real evidence tends to perform across all three. Some practitioners argue AEO and GEO are close enough to be the same discipline with different names; others draw a cleaner line at which system does the citing (Google’s own features versus third-party LLMs like ChatGPT and Perplexity). Search Savvy’s AEO, GEO and AI search glossary covers these adjacent terms in more depth if your team needs shared definitions before briefing a project. Search Savvy treats GEO as one part of a wider AI-search strategy, alongside classic technical SEO, rather than a standalone replacement for it.

How Does ChatGPT Search Ranking Actually Work?

ChatGPT search ranking, and generative answer construction more broadly, works in two loosely separable steps: a retrieval step that decides which pages to consider (closer to classic information retrieval, weighing relevance and, for web-connected tools, real-time search results), and a synthesis step that decides which specific spans from those pages to cite. GEO tactics mostly act on the second step. You have far less control over whether a page gets retrieved at all than you do over whether, once retrieved, your specific sentence is the one an LLM chooses to lift into its answer.

This is why the practical GEO advice below is about the content itself, not about tricking a retrieval algorithm: once you’re in the candidate pool, competing for the citation is a content-quality problem.

Practical GEO Steps for Marketers Today

Given the research above and Google’s own public guidance on optimizing for generative features, here is what to actually do:

1. Answer the likely question directly, early in the content. A generative engine synthesizing an answer needs a clean, self-contained statement it can lift; bury the answer in the fourth paragraph and it’s less likely to be extracted.
2. Add real, attributable statistics. Not invented ones. Cite the study, the year and, where possible, the exact source.
3. Include genuine expert quotes, correctly attributed, when they add real value, not decorative quotes for their own sake.
4. Cite your own sources explicitly rather than making unattributed claims, since the “Cite Sources” strategy produced strong, consistent gains in the founding study.
5. Write clearly. Fluency and readability improvements produced measurable gains with zero new content added, the cheapest lever on this whole list.
6. Keep technical SEO solid. Google has been explicit that pages need to be indexed and eligible to appear with a snippet before they can show up in its AI features at all, so none of this replaces a working technical SEO audit.
7. Structure content around real topics, not query variations. A content strategy built on genuine topical coverage gives an LLM more legitimate material to draw from than dozens of near-duplicate pages built to catch keyword permutations.

Common Mistakes to Avoid

• Treating “add a statistic” as license to invent one. The lift came from genuine, verifiable evidence in real applications; fabricated stats risk your brand’s credibility the moment someone checks.
• Assuming GEO replaces SEO. Retrieval still depends heavily on classic signals like indexability and relevance.
• Chasing every AI platform’s rumored ranking factors. Platforms change fast and rarely document their methods; anchor advice in the peer-reviewed research and each platform’s own public statements where they exist.
• Ignoring that top-ranked pages can lose visibility. Being position 1 in classic search doesn’t guarantee the citation in a generated answer.
• Confusing GEO, AEO and AI Overview optimization as identical. The tactics overlap, but the target systems and measurement differ.

Frequently Asked Questions

Who Coined the Term Generative Engine Optimization?

Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, researchers affiliated with Princeton University, Georgia Tech, the Allen Institute for AI and IIT Delhi, in a paper posted to arXiv in November 2023 and later presented at KDD 2024.

Is GEO the Same as AEO?

They’re closely related and the industry often uses them interchangeably, but the cleaner distinction is target surface: AEO usually refers to direct-answer formats like featured snippets and voice search, often within Google’s own ecosystem, while GEO usually refers to citation inside synthesized answers from generative AI tools like ChatGPT and Perplexity.

Does Adding Fake Statistics Improve GEO Performance?

The founding research measured a visibility lift from adding statistics and quotes, but its own experimental prompts explicitly permitted fabricated data to test the mechanism. That is not advice to fabricate content in practice; use real, attributable evidence, since a fabricated citation that gets surfaced becomes a real credibility problem for your brand.

Do I Need to Rank Number One on Google to Be Cited by AI Tools?

No. The founding study found lower-ranked sources (around position 5) sometimes gained the most from optimization, while the top-ranked source in the same set occasionally lost visibility. Generative engines weigh content quality signals in the synthesis step separately from classic ranking position.

How Is GEO Different From Traditional SEO?

SEO optimizes for a position on a list of ranked links; GEO optimizes for being one of the sources an AI system cites when it writes a synthesized answer. In practice, the underlying content-quality work overlaps heavily, but the success metric and the surface differ.

Can I Measure GEO Performance Directly?

Not as precisely as SEO rankings yet. Some platforms, including Google Search Console’s Generative AI performance reports, show impressions in AI-generated features. Beyond that, most GEO measurement today relies on manually checking whether your brand or page is cited across a sample of AI tool responses, since third-party platforms rarely publish citation data.

The Bottom Line

Generative Engine Optimization is a real, research-backed discipline: get your content indexable, answer questions directly and early, back claims with genuine, attributable evidence, and write clearly, while treating any advice that implies inventing statistics or quotes as a misreading of the founding study. This week, pick one page you’d want an AI tool to cite, and check whether it makes a direct, verifiable claim in its first few sentences with a real source attached. If you want a structured approach across your site, Search Savvy’s AI search optimization services are built around exactly this kind of work.