Generative engine optimisation, or GEO, is the practice of making your content easy for an AI system to retrieve, quote and cite, so that your brand gets named in the answers produced by ChatGPT, Perplexity, Google’s AI Overviews, Gemini and Copilot. The unit of success is a citation rather than a click.

That second sentence is the part people skip. It changes everything downstream. A page that earns forty visits and gets quoted in thirty answers is doing the job. A page that earns four hundred visits and gets quoted in none is not.

The wider subject, including how the four competing labels relate to each other and what has actually been measured about who gets cited, is set out on answer visibility.

Where the term came from

It came out of a paper, which is unusual for this industry.

“GEO: Generative Engine Optimization”, by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, was first posted to arXiv in November 2023 and published at KDD 2024. It proposed the name, built a benchmark called GEO-bench and reported that its methods could raise a source’s visibility in generated answers by up to 40%.

Treat that 40% as what it is. A measured result on the authors’ own benchmark, using their own visibility metric, against a set of queries they assembled. Not a figure anyone has reproduced on a commercial site. Not one to quote at a board.

The useful part of the paper is not the number. It’s the framing: visibility inside a generated answer is a thing you can define, measure and move.

What Google says about it

Google published its own guidance. It is blunter than most agency copy on the subject.

From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.

And on the mechanism:

The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems.

The same page names two mechanisms. Retrieval-augmented generation, where the model pulls indexed pages through the existing ranking systems and then writes an answer with links to them. And query fan-out, where the model generates a set of related queries at once and gathers results for all of them, which is why an AI answer can cite pages you never ranked for on the original phrase.

It also lists what a page has to satisfy to be eligible at all:

  • indexed in Google Search
  • eligible to be shown with a snippet
  • meeting Search’s technical requirements
  • the site included in Search generative AI features, which you can check in Search Console

Two things it says you do not need. Content chopped into tiny fragments for the model’s benefit. And an llms.txt file, which Google states it does not use and which has no effect on visibility in Search.

None of that means GEO is a marketing invention. It means that for Google’s surfaces the entry ticket is ordinary technical health. The competitive work starts after you have it.

GEO, AEO, LLM SEO, AI SEO

Four labels, one job, no settled boundary between them. Wikipedia’s entry on the subject records that no consensus definition separating the terms had been established in the academic literature as of early 2026. In practice they are used interchangeably.

Anyone drawing a firm line between GEO and AEO is describing their own service, not an agreed distinction. That is fine. It is worth knowing which you are being sold.

Both of the other labels have been tested against the search results rather than argued about: what answer engine optimisation is and how it differs from GEO counts the domain overlap between the two result sets. LLM SEO, explained without the jargon checks whether the pages teaching the third label are cited by the models at all.

What decides whether a model quotes you

Four things, in the order they bite.

You have to be retrievable. Indexed, snippet-eligible, technically sound. This is the boring half. It is where most of the failures are. A page a crawler cannot read is not a candidate for anything.

The answer has to be liftable. Look at what actually gets cited. On 10 August 2026 the UK AI Overview for “ai visibility” linked six sources: two Semrush properties, three explainer pages from Frase, LLMPulse and Onclusive, plus a YouTube video. The three explainers all answer the question in their opening lines. A model assembling a paragraph takes text that already reads like an answer. A page that spends four hundred words building to the point gives it nothing to take.

Most of the sources are not yours. Ask a model who the best generative engine optimisation agency in the UK is and it does not read ten agency websites and form a view. It reads the pages that have already compared them. On that exact query the AI Overview names eight agencies. Four of the ten organic results are lists written by other agencies about themselves and their rivals. Your own site is one input among many. Often it is not the decisive one. We looked at who those agencies are and how often AI actually cites them, which is a more interesting question than the lists answer.

Retrieval is per-query and probabilistic. Ask the same question twice and you can get two different shortlists. Being named once is not being named. Any measurement built on a single run is noise.

What GEO does not do

It does not replace SEO. Every keyword in this category returns an AI Overview with a set of ordinary blue links underneath it. Buyers use both. How far the two halves of the page overlap is measurable: across twelve UK search results, 54% of the sources cited did not rank beneath the answer.

It does not fix demand. If nobody is asking the question, winning the answer is worth nothing. Some of the prompts in this category carry a few dozen searches a month. Being cited in an answer nobody requests is a vanity metric with better vocabulary.

It does not fix what happens after the click. You can be named first in every answer and still lose the sale on a page that does not convert.

And it does not arrive quickly. The pages being cited today were mostly published months ago, indexed, linked and then retrieved.

How you know it is working

Not by traffic. Traffic will barely move, because the whole point of an AI answer is that the user does not need to visit you.

You know by counting citations. There are only a few honest ways to do that. We wrote up what AI visibility means and how to measure it, covering the five methods in commercial use, what each one sees and where each one lies to you.

If you want the cheapest version, run the buying questions yourself and read the sources under the answers. The afternoon method is here. It costs nothing and it beats any vendor’s dashboard as a starting point, because you choose the prompts and you can see who is being named instead of you.

One note on the British spelling

Ahrefs, 10 August 2026, UK market. “Generative engine optimisation” returns 1,300 searches a month at keyword difficulty 0. “Generative engine optimization” returns the same 1,300 searches a month in the UK, at difficulty 69.

Same concept, same country, same volume, sixty-nine points apart. Difficulty is calculated from the pages that currently rank. The two spellings return different pages. So the gap says nothing about language. It says how young the category is: on the British spelling, almost nobody has bothered to write the page.

That will not last. It is worth knowing while it does.