What is Generative Engine Optimization (GEO)? A Definition You Can Check

• GEO is about earning a place inside AI-generated answers, not on a page of links.
• The term comes from a peer-reviewed 2024 research paper, which found the right content changes can lift visibility in AI answers by up to 40%.
• GEO is not geo-targeting, not keyword stuffing, and not a one-time project.
• Because AI engines give different answers to the same question, GEO is as much a measurement discipline as an optimization one.

The shift GEO responds to

For twenty years, being found online meant one thing: rank on the page of links a search engine returns, then win the click. That model is being replaced in front of us. OpenAI reports that ChatGPT has more than 900 million weekly active users. Google says AI Overviews, the AI-written answers above its classic results, are used by over 2 billion people a month.

When a buyer asks one of these engines “what tool should we use for X?”, the engine does not return ten blue links. It writes an answer, and that answer names a handful of brands. Either you are one of them or you are not. There is no page two of an AI answer.

That is the problem GEO exists to solve: how does a brand earn, keep and measure its place inside answers it does not control?

Where the term comes from

Unusually for a marketing term, GEO has a precise birthplace. It was coined in the research paper “GEO: Generative Engine Optimization” by Pranjal Aggarwal and co-authors from Princeton University and IIT Delhi, published at KDD 2024, one of the main academic venues for data science.

The paper did three things that still shape the field:

1. It defined the problem. The authors formalized the “generative engine”: a system that searches for sources, then writes a synthesized answer with citations. That covers ChatGPT with browsing, Perplexity, Google’s AI Overviews and AI Mode, and Copilot.
2. It measured what works. The authors tested nine ways of rewriting the same content and measured how visible that content became in generated answers. Their headline: the right changes “can boost visibility by up to 40%”. The best-performing changes were adding quotations, adding statistics, and citing sources. Old-style keyword stuffing did not help, and on some engines it hurt.
3. It gave the field a benchmark. GEO-bench, a set of 10,000 realistic queries, so later work could be compared fairly.

We cover the findings, and their limits, in chapter 4.

What GEO is not

Not geo-targeting. In advertising, “geo” has long meant location targeting. Same letters, unrelated discipline. Generative Engine Optimization is about AI answers, not maps.

Not keyword stuffing with new packaging. The tactic most associated with old SEO was measured directly in the founding paper and classified as non-performing. Repeating words does not persuade a system that reads for meaning.

Not a replacement for SEO. AI engines still lean on search and retrieval to find sources, so classic discoverability still matters. GEO extends SEO to a new surface; it does not delete it. The differences are the subject of chapter 2.

Not a one-time project. AI answers change between runs, between model versions and between months. A brand that “checked its AI visibility once” knows what one answer said one time. GEO in practice is a loop: measure, fix, re-measure.

One discipline, several names

You will also see AEO (Answer Engine Optimization), LLMO (Large Language Model Optimization) and “AI SEO”. These names describe the same discipline with different emphasis, and vendors tend to pick the one they can own. GEO is the name with the academic paper behind it and the widest adoption. We use GEO throughout this guide and treat AEO as a synonym; the differences that actually matter are between optimizing for rankings and optimizing for answers, not between the labels.