Your page ranks well on Google. You ask ChatGPT the same question, and it cites three other sites. Not yours. That frustration has a name, and the beginnings of a method: generative engine optimization, or GEO.
GEO covers the practices that raise the odds of your content being reused, summarised or cited inside an answer written by a generative AI. Where SEO chases a position in a list of links, GEO chases a citation inside a written answer. Both are worked on the same pages, with criteria that overlap heavily.
Here is what really changes when an AI sits between your content and your reader, which levers have actually been measured, and which ones Google calls useless. No prior knowledge needed: every piece of jargon gets explained along the way.
GEO, AEO, LLMO: five names for one thing
The vocabulary is a marketing battlefield. The Wikipedia page on generative engine optimization lists several competing labels for an identical practice.
- AEO, answer engine optimization
- LLMO, large language model optimization
- AIO, artificial intelligence optimization
- AI SEO, plain and simple
No definition has settled the difference between these acronyms, and practitioners swap them freely. Nikhil Lai, an analyst at Forrester, wrote in 2025 that these approaches differ from SEO in notable but not fundamental ways. He added that the people pushing these acronyms overstate the gap to carve out a slice of marketing budgets.
If an agency pitches you a GEO service fully separate from your SEO, ask which concrete actions it would take that it would not take in SEO. The answer is usually very short.
How an AI picks what it cites
Google published official documentation on optimising a website for generative AI features in Search. It describes two mechanisms that explain most of the behaviour.
The first is RAG, retrieval-augmented generation. The system first pulls pages from the regular search index, then writes its answer from what those pages contain. Direct consequence: a page missing from the index has zero chance of being cited.
The second is query fan-out: the model turns your question into several parallel searches. Google gives the example of a lawn overrun with weeds, which triggers searches on the best weed killers, on chemical-free methods, on prevention.
So you are no longer competing on one query, but on a sub-question you never anticipated. A page that digs into a problem, edge cases included, has far more entry points than a page that restates the headline question.
SEO is not replaced, and Google says so in writing
Google's position in that documentation is blunt: optimising for generative AI search is optimising for the search experience, so it is still SEO. The AI features lean on the same ranking and quality systems as regular results.
In practice, the pages cited by conversational engines are often the ones already sitting at the top of the results for the same question. If your SEO foundations are weak, no GEO trick will paper over them. Our beginner's guide to search engine optimisation covers those basics.
| Question | SEO | GEO |
|---|---|---|
| What you aim for | A position in a list of links | A citation inside a written answer |
| Unit of work | The page, for one query | The passage, for one sub-question |
| Success signal | Clicks and impressions | How often you are cited or mentioned |
| Known criteria | Published by Google | Partly published, often inferred |
The real gap is traffic. A complete answer often saves the reader from opening the source at all. You gain reputation and credibility before you gain visits, which makes measuring the payoff much blurrier than in SEO.
The levers that have been measured
A team of researchers published the first experimental work on the topic in 2024, presented at the KDD conference in Barcelona. Their paper, GEO: Generative Engine Optimization, tests content changes and measures visibility before and after.
Three interventions stand out clearly in their tests. Citing your sources comes first. Strengthening the authority of the writing follows. Adding statistics completes the trio. The authors report visibility gains of up to 40% on some categories of queries.
That "up to 40%" is a relative gain on a visibility metric computed over a test set of queries, not a market share. Content moving from 5% to 7% visibility gains 40% in relative terms and is still barely cited. The number points to a direction of work, it promises no volume.
On the ground, these findings turn into simple habits. Put the answer at the start of the paragraph, before the explanation. Link the source of every number. Date your data. Write headings that announce an answer rather than a theme.
Google, for its part, keeps pointing at helpful, non-generic content. Its systems read several sources at once, so a take that already exists elsewhere adds nothing. A view grounded in your own practice, with its numbers and its failures, stands apart.
Readability matters too, for humans and machines alike. Short sections, explicit subheadings and clean markup make a specific passage easy to lift. If you are starting from scratch there, learning to structure your pages in HTML gives you durable ground.
One last lever, counter-intuitive for a brand: impartiality. On questions like "what is the best solution for", pages that compare options honestly, competitors included, get picked up more readily than pages that sell.
The fake tricks to ignore
Google devotes a whole section of its documentation to misconceptions. It is worth reading before you spend time or money.
- The
llms.txtfile and other special AI markup are not used by Google Search - Chunking your content into small blocks is unnecessary, the systems handle several topics on one page
- Rewriting your style for machines serves no purpose, the models handle synonyms and general meaning
- Chasing manufactured mentions across the web works far less well than it sounds
- Structured data is not required for AI answers, though it stays recommended for rich results
On mentions, one nuance is due. A company quoted in a serious publication, on a specialist forum or in genuine customer reviews earns credibility, and that reputation travels into AI answers. It is the artificial manufacturing of mentions that is a bad bet.
Google also states that spinning up a separate page for every question variant, to cover every possible sub-query, falls under its spam policies on scaled content abuse. Piling up thin pages exposes you to a penalty, not to citations.
Measuring whether you get cited
Two free tools give you actual numbers. Google Search Console offers a performance report for generative AI features. Bing Webmaster Tools added an AI Performance report of its own. Each one only covers its own surfaces.
For ChatGPT, Perplexity or Mistral, you check by hand. Build a list of ten to twenty questions your customers genuinely ask, then run it again at regular intervals. Four indicators are enough to start.
- Presence: does your site show up in the answer?
- Frequency: on how many questions from the list?
- Role: are you recommended, or merely listed among others?
- Source: does the citation point to your page, or to a site talking about you?
That check takes an hour a month. To automate it, you can build an automation with n8n that sends the question list every week and stores the answers it gets back.
Generative engine answers shift from one session to the next, depending on wording and account history. A single test proves nothing, either way. Only repetition over several weeks gives you a usable trend.
Where to start when you work alone
Rebuilding an entire site for GEO would be a poor trade. The practices are still moving, measurement is imperfect, and the fundamentals have not changed. A three-step progression saves you from burning weeks.
Start with your three most commercially useful pages. Give each one a direct answer up top, linked sources, dated figures. Then check they are indexed and load fast, because nothing else counts otherwise.
Move on to the side questions you have never covered. Those are what feed the query fan-out described above. A recurring customer objection, an edge case, a price people misread: each deserves a section, not necessarily its own page.
Finally, keep a written record of your citation checks. Without a noted starting point, you will have no way of saying in six months whether your work produced anything.
Frequently asked questions
Does GEO replace SEO?
No. In its official documentation on generative AI features, Google writes that SEO best practices still apply, because those features run on its usual ranking systems. GEO looks more like a specialisation of SEO than a separate discipline.
Should you create an llms.txt file on your site?
Google states that Google Search, including its generative AI features, does not use llms.txt files or special markup aimed at language models. It is not priority work if your goal is being cited.
How do you know if ChatGPT or Gemini cites your site?
Manually test a list of ten to twenty questions your customers ask, and note whether your site appears, on how many questions, and in what capacity. On the data side, Google Search Console offers a performance report for generative AI features, and Bing Webmaster Tools an AI Performance report.
What is the difference between GEO, AEO and LLMO?
All three acronyms describe the same practice: making content citable by a generative AI. No definition has settled how to tell them apart, and practitioners use them interchangeably.
Can GEO work for a small site with no budget?
Yes, provided you bet on what big sites do not produce. Google recommends non-generic content, grounded in real experience rather than summarising what already exists. A niche subject covered in depth, with sourced figures, is more citable than one more general article.
