What is generative engine optimization?
Generative engine optimization, usually shortened to GEO, is the practice of making a page more likely to be used and cited when an AI assistant writes an answer. It works on the same raw material as search optimisation, a page on the open web, but the target is different: rather than ranking a link for a person to click, you are trying to be the passage a model quotes. The term comes from a 2023 research paper and the practice around it is young, so any confident method claim, including the ones here, is provisional.
Where the term generative engine optimization came from
The phrase was coined in a 2023 academic paper that treated AI answer engines as a new surface with its own optimisation problem, built a benchmark of queries, and tested what happened to a page's presence in generated answers when its content was changed in specific ways.
The finding that survives repetition is unremarkable and useful: the changes that helped were the ones that made a passage more usable as evidence. Adding citations, adding quotations, adding sourced statements. Adding keywords did comparatively little. The paper is linked in full on the LLM visibility page, and reading the original is worth the twenty minutes, because most of what is written about GEO is a paraphrase of a paraphrase of it.
Two caveats belong with it. The study tested a limited set of systems at one moment in time, and those systems have changed since. And a benchmark result is not a promise about your page.
GEO vs SEO
They are not opposites, and treating GEO as a replacement for search optimisation misreads what is going on underneath. Generated answers are still assembled from crawled, indexed, ranked web pages. If a page cannot be found, it cannot be cited.
| Search optimisation | Generative engine optimization | |
|---|---|---|
| Unit of success | A ranked link | A quoted or cited passage |
| What the reader sees | A list to choose from | One composed answer |
| What you optimise | The page as a whole | Self-contained passages within it |
| What signals help most | Relevance, links, technical health | The same, plus clarity and attribution |
| How it is measured | Position for a keyword | Presence across sampled answers |
| How fast feedback arrives | Days to weeks, trackable | Slower and noisier, sampled |
The practical reading is that GEO is a layer on top of ordinary publishing quality, not a separate discipline with separate tricks. Google Search Central on AI features and how content is used says as much from the platform side: the material eligible to appear in AI features is the material already eligible to appear in Search.
Citation earning: what makes a passage usable
Think about what a system needs in order to safely reuse something you wrote. It needs a passage that answers a question completely enough to stand alone, in language that does not depend on the three paragraphs above it. It needs a claim it can attribute, which is why sourced statements travel better than assertions. And it needs to be confident the passage is about the entity the user asked about, which is a naming problem as much as a content one.
That is why the most reliable GEO advice sounds like editing advice. Answer the question in the first paragraph under the heading. Define the term before using it. Put the number next to its source. Keep one idea per section. None of this is a trick, and all of it also makes the page better for a person.
Structured content a machine can parse
Structure is the cheap half of the work. Headings that state the question rather than a slogan. Lists where the content is genuinely a list. Tables where the content is genuinely comparative. Consistent entity naming, so the same legal name, address, and description appear everywhere you are described. Valid structured data, which does not manufacture authority but does remove ambiguity about what a page is.
None of these force a citation. They remove reasons not to use you, which is a smaller claim and a more honest one. It is also worth knowing what the retrieval step is actually doing while it reads: OpenAI's documentation on web search in its models describes a system that runs a search, reads a small number of results, and composes from them. That is a much smaller window than most people picture, and it is why being clear beats being long.
AI answer visibility is the outcome, not the method
Visibility is what you measure afterwards, and it is a separate page on this site for a reason. GEO is the work; LLM visibility is the reading. Keeping them apart matters commercially, because a package sold on the promise of visibility should be able to show its measurement method, and one sold as GEO should be able to describe what it actually changes on the page.
What GEO is not
It is not control of what a model says. Nothing published on a website reaches into a trained model, and no method promises a citation in a specific answer.
It is not a removal route. If the problem is a false statement, a policy-breaking review, or a piece of personal information you want gone, GEO does not address it. Those have their own routes, some of them free: Google's Results About You tool is one example of a direct request channel for personal information in Search, and it has nothing to do with optimisation.
And it is not a reason to publish more of the same thin content faster. The one finding everyone agrees on is that generated answers reward material that is specific, sourced, and clear. Volume without those does not appear to earn anything.
If what you are dealing with is not a visibility problem at all but something false or unwanted that keeps surfacing, that is a different piece of work, and it is the first thing a reputation audit separates out.
Questions about what is generative engine optimization (geo)?
What is generative engine optimization?
The practice of making a page more likely to be used and cited when an AI assistant writes an answer, mainly by making passages self-contained, specific, and clearly sourced.
How is GEO different from SEO?
Search optimisation aims at a ranked link a person clicks. GEO aims at a passage a model quotes inside one composed answer. The underlying index and crawl are the same, so the two overlap heavily.
Does GEO work?
The published research reports gains for content changes that add citations, quotations, and sourced statements. It tested a limited set of systems at one point in time, so treat it as a direction rather than a formula.
Can GEO get a bad result removed from an AI answer?
No. It can influence what gets cited. Removing or correcting something false or policy-breaking is a separate process that runs through the platform or the publisher, not through optimisation.