GEO, AEO and LLMO: what the terms mean and where off-page work fits

Generative engine optimisation, or GEO, is the work of getting your brand named and your pages cited in answers written by AI assistants. Answer engine optimisation (AEO) and large language model optimisation (LLMO) are other names for the same job. None has an official definition, and the people who use them rarely mean different things.

It differs from SEO in what counts as a result. SEO aims at a position in a ranked list and a click. GEO aims at a sentence inside an answer, usually with no click. That shift moves much of the work off your own site, which is why it belongs in a course on off-page SEO.

The terms

Term	Stands for	Where it came from	How it is used now
AEO	Answer engine optimisation	The era of featured snippets and voice assistants, before generative AI	Any work aimed at being the direct answer
GEO	Generative engine optimisation	A research paper first posted in 2023 and presented at the KDD conference in 2024	The most common label, used by tool vendors and agencies
LLMO	Large language model optimisation	Practitioner usage	Usually stresses the model’s memory, meaning what it learned in training
AI visibility	No acronym	Tool vendors	The measured outcome: how often you appear in answers

Some writers draw a line between optimising for retrieval (what the assistant looks up when asked) and for memory (what the model learned in training). That is a real distinction, covered below, but the acronyms do not map to it reliably. On this site we say “AI visibility” for the outcome and avoid the acronyms where we can.

Where the term GEO comes from

The paper that introduced the name is “GEO: Generative Engine Optimization”, by researchers at Princeton, IIT Delhi and other institutions, presented at KDD 2024. It tested changes to page content against a benchmark of generative answers. Adding quotations, statistics and citations to sources raised a page’s visibility by up to 40%. Lower-ranked sources gained most: a site ranked fifth gained 115% from citing sources, while the first-ranked site lost 30%, according to figures from the paper as summarised by Peec AI.

Three limits matter. It was a lab benchmark using systems from 2023. It tested on-page changes only. And the effects varied by subject area. The paper named the field. It did not test off-page factors at all.

How GEO differs from SEO

	SEO	GEO
The result	A ranked position for a query	A mention or citation inside a written answer
The unit	One page, one keyword	A brand, across many prompts
What the user sends	A short query with known search volume	A long prompt, rewritten by the assistant into several searches that keyword tools do not show
What gets you there	Relevance, links, page quality	Being retrievable, plus what third-party pages say about you
Stability	Positions move slowly	Answers vary from one run to the next
The payoff	Clicks	Mostly influence without clicks
The metric	Rank and traffic	Mention rate and citation rate over many runs

The mechanics behind the third row are covered in how AI search chooses sources. In brief, an assistant decides whether to search, rewrites the prompt into several queries, retrieves pages from a search index, and writes an answer from the passages it selects.

That pipeline produces two outcomes, and the terms blur them:

• A mention is your brand named in the answer text. It draws on the model’s memory and on whatever the retrieved pages say about you.
• A citation is your URL listed as a source. It depends on retrieval.

Semrush’s 2026 AI Visibility Index, based on 126 million US prompts, found that on Gemini the overlap between mentioned brands and cited domains was as low as 30%. A plan that only chases citations to your own site misses most of the mentions.

What stays the same

Assistants that search the web retrieve from search indexes, so a page that cannot be crawled and indexed cannot be cited. Rankings still feed retrieval, and rankings still depend on links.

Google’s own position is that nothing new is required. Its documentation on AI features says: “You don’t need to create new machine readable files, AI text files, or markup to appear in these features.” On llms.txt, a proposed file for AI crawlers, SE Ranking’s November 2025 study of 300,000 domains found no relationship with how often a domain was cited.

Google’s John Mueller has been blunt about the naming. PPC Land reported that he wrote on Bluesky on 14 August 2025: “The higher the urgency, and the stronger the push of new acronyms, the more likely they’re just making spam and scamming.”

Where off-page work fits

This is the part the acronyms hide. A brand’s own pages are only one of the things an assistant reads. The rest is other people’s pages, and the public evidence leans heavily that way. Nearly all of it is correlational and published by companies that sell AI visibility tools.

Claim	Status of the evidence
Brand mentions across the web track AI mentions closely	Correlational, large sample (Ahrefs, 75,000 brands, December 2025)
Mentions on YouTube are the strongest single correlate	One vendor study
Raw backlink counts are a weak correlate	Ahrefs 2025, Seer Interactive 2025
Links no longer matter	Not supported. Links with the brand as anchor correlate well, and rankings feed retrieval
A place in third-party “best” lists lifts visibility	Vendor data, correlational (Peec AI, 2025 to 2026)
Review volume drives recommendations	Speculative. No public study isolates it
An llms.txt file helps	Evidence against

So the off-page side of GEO is a familiar list with a different measure of success:

• earning coverage and mentions through digital PR
• getting included in third-party “best X” lists
• being reviewed, which reviews as an off-page signal covers
• being discussed on Reddit and YouTube
• keeping the facts about your brand consistent wherever they appear

Practitioners sometimes call this “LLM seeding”. The honest version earns those placements. The dishonest version manufactures them, and it now has a policy against it.

The policy line

On 15 May 2026 Google changed the definition in its spam policies. Spam now includes “attempting to manipulate generative AI responses in Google Search”. Google presented this as a clarification of existing rules.

For off-page work the reading is simple. Earning a mention is fine. Fake community posts, undisclosed paid placements and hidden instructions aimed at assistants are manipulation. Link building for AI visibility lists the practices observed so far.

How it is measured

There is no position to track. SparkToro’s January 2026 research found that the same list of brands came back less than 1% of the time when a prompt was repeated. The working method is to fix a set of prompts, run each many times, and report how often you are mentioned and how often you are cited, as two numbers. The AI visibility tools comparison lists the trackers that do this.

Where to go next

Start with brand mentions and citations for the signal that the evidence favours most. The glossary defines mention, citation, fan-out and the other terms used here.