Citino teamWhat GEO means
Generative engine optimization is the practice of improving how often, how early and how accurately generative AI names your brand in its answers. The name comes from the paper “GEO: Generative Engine Optimization” by Aggarwal and colleagues, first posted in November 2023 and accepted to KDD 2024.
You will see three names for the same work:
| Term | Stands for | Where it comes from |
|---|---|---|
| GEO | Generative engine optimization | The 2023 research paper above |
| AEO | Answer engine optimization | Older; first used for featured snippets and voice assistants |
| LLMO | Large language model optimization | Industry shorthand |
The labels matter less than the goal. When a buyer asks an assistant “what is the best tool for X?”, you want to be in the answer, near the top, described the way you would describe yourself.
How it differs from SEO
SEO is about ranking a page in a list of links. GEO is about being chosen inside a written answer. That changes three things.
The unit of success. In search you win a position. In an AI answer you win a mention, and usually one of only a handful. An answer that lists three brands has no page two.
The result is not stable. Ask the same question twice and you can get different brands, in a different order. A single check tells you very little; you need many runs to see a rate. (See how to measure AI visibility.)
Your own site is only part of the input. When assistants search the web, they read review sites, directories, community threads and comparison articles as well as your pages. What those pages say about you shapes the answer.
What does not change: engines that search the web still read pages from an index. Google states plainly that there are “no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary”. If a page cannot be crawled and indexed, it cannot be cited.
What the research says works
The GEO paper tested nine ways of rewriting a source page and measured how much more visible it became in generated answers, on a benchmark of real search queries. The clearest results:
| Change to the page | Effect on visibility in the paper |
|---|---|
| Add quotations from relevant sources | Among the largest gains, around 40% on the paper’s main measure |
| Add statistics | Large gains, around 30% |
| Cite sources | Clear gains, largest for lower-ranked pages |
| Improve fluency | Moderate gains |
| Keyword stuffing | Little or no improvement |
Two findings are worth keeping in mind. First, pages that ranked lower gained much more than pages already at the top. Second, the gains varied by subject, so no single tactic works everywhere. Treat the numbers as evidence of direction, not a forecast for your site.
Specific, sourced, plainly written facts get used. Repeating keywords does not.
What GEO work looks like in practice
Most of the work falls into four kinds of task:
- Measure. Track the questions your buyers ask, on each engine, every day. Record whether you are named, your position, the tone, and which pages the answer read.
- Find the gaps. For questions you lose, look at what the winning answers rely on: a review site you are missing from (a source gap), or a claim your pages never make (a content gap).
- Fix them. Get listed, correct what is wrong, and state your facts plainly on the pages engines read.
- Prove it. Check whether the fix moved your numbers by more than chance, and by more than competitors moved over the same days. (See how to tell if a fix worked.)
What to avoid
Google’s own guidance warns against writing separate content “for AI”, breaking pages into fragments for machines, and producing pages at scale to manipulate results. Its spam policies apply to AI features as they do to Search. Fabricated reviews, invented statistics and fake “independent” lists can also get a brand ignored by the assistants that weigh sources most carefully.
The durable approach is the boring one: true, specific, well-sourced pages, on a site engines can read, backed by what other people say about you.