GEO, AEO, LLMO:what the termsreally mean

GEO (generative engine optimization), AEO (answer engine optimization), LLMO (large language model optimization) and AIO describe the same goal from slightly different angles: your brand should appear in AI answers, and it should be described correctly. Only GEO has a traceable origin, a research paper from 2023. AEO grew out of answer boxes and voice assistants, while LLMO and AIO are industry labels without a fixed definition. So the acronym is not what matters for your business. What matters is a question you can measure: are you named when customers ask an AI for a solution like yours?

Nikolai Schöbel and Jeremias Burger

Co-founders of Scalableloops GmbH. Nikolai Schöbel leads online marketing and AI strategy, Jeremias Burger the AI architecture. Both build AI systems and train teams on them in their own agency work.

Where does the term generative engine optimization come from?

GEO is the only one of the four terms with a clearly datable origin. In November 2023 a team from Princeton University and the Indian Institute of Technology Delhi published the paper “GEO: Generative Engine Optimization” on the preprint server arXiv. In August 2024 it was presented at KDD, a major data mining conference.

For the study the researchers built a benchmark called GEO-bench with around 10,000 queries from many subject areas. They rewrote web content following different patterns and measured how much of it then showed up in the answers of a generative search system. According to the paper's abstract, visibility rose by up to 40 per cent. The findings were also checked against Perplexity, a publicly used system.

Three patterns worked best: citing sources, adding direct quotations and replacing vague claims with concrete figures. Classic keyword stuffing, repeating a search term over and over, brought little to no improvement. That is the real takeaway for practice: what an AI likes to cite looks more like a well-sourced trade article than a page tuned for keywords.

One caveat belongs here. The paper measures under lab conditions, research has moved quickly since, and the gains vary by subject area. The study shows a direction, not a guarantee for any single page.

LLMO and LLM SEO: a discipline of its own or a new label?

LLMO shifts the focus from the search system to the language model. IONOS describes LLMO as structuring and writing content so that large language models can understand, reuse and reference it. Evergreen Media uses it for everything that improves how a brand appears in chatbots such as ChatGPT, Gemini or Claude, and lists GEO, AEO and AIO as related labels.

That makes LLMO a working term rather than a standard. Both sources treat it as a label in a field that is still sorting itself out. LLM SEO is another, equally loose name from the same practice.

From our own work comes a distinction that matters in practice: a language model can talk about you in two ways. Either from what it learned in training, or from pages it searches live on the web while answering. That is why our AI visibility analysis records whether a system answered with or without web search. The results can differ considerably, and only the second route can be influenced in the short term through your website and through mentions on other sites.

GEO vs AEO vs LLMO vs AIO side by side

The table sums up what the acronyms stand for, where they come from and what they aim at. Treat the split as orientation, not a standard, because binding definitions do not exist yet.

Which term matters for your business?

Honestly, none of them. A buyer asking an AI for a supplier does not care which acronym you file your content under. What counts for you is whether your company appears in the answer, in what position, in what tone and with correct details. That can be measured, whether your provider calls it GEO, AEO or AI SEO. For an overview of the work on your own website, see our article on AI SEO.

In our AI visibility analysis we put questions to several AI systems the way real customers would ask them, in four forms: the recommendation question asking for a suitable solution, the problem question in everyday language, the scenario from the point of view of a role in a company, and the specialist question about a standard or specification. For technical suppliers the last one often separates the leaders from the rest.

We evaluate along a few clear metrics: how often are you named compared with competitors? Are you merely mentioned or cited as a source? Are the facts about your company right? In what tone does the AI talk about you, and where do you sit in a list of recommendations? How this works in detail is shown in Measure AI visibility.

One observation from our audits that matches the GEO research: your own website is only one of many sources. A machine builder with an excellent product page can still be missing from AI answers if trade media, directories and industry portals do not mention it. That is why our GEO audit always covers both sides: your own content and the mentions on other people's sites.

1. 01

   Measure

   Step 1

   Put customer-style questions to several AI systems and record who gets named.

2. 02

   Assess

   Step 2

   Evaluate mentions, citations, accuracy and tone against competitors.

3. 03

   Improve

   Step 3

   Revise your content with clear answers, figures and sources, and build mentions on third-party sites.

4. 04

   Repeat

   Step 4

   Measure again at fixed intervals, because AI answers keep changing.