GEO (Generative Engine Optimization)
GEO is the practice of designing content and structured data so a brand appears in the information generative AI draws on when composing an answer; it is used almost interchangeably with AEO.
Where does the term come from?
From a paper published in 2023 and presented at KDD the following year, "GEO: Generative Engine Optimization". Its starting point was that, unlike optimisation aimed at ranking in a list of results, generative engines compose an answer — so what matters is which documents they consult and how they cite them. In practice the term is now used far more broadly than the paper intended, covering all the work of getting a brand to appear in answer surfaces such as ChatGPT or Google's AI Overviews.
Is it different from AEO?
In practice they mean much the same thing. If you must separate them, AEO focuses on being the result that gets cited, GEO on being a source consulted during generation. The work is effectively identical. More important than the distinction: neither replaces SEO. Most documents an AI consults are documents it found through search, so if you are invisible in search you are absent from the answer.
Where do you start?
Build definitional content first, attach structured data second, then confirm AI crawlers can reach it. Files such as llms.txt, which summarise site structure for AI, belong to that third step. The order runs that way because cost falls and scope narrows as you go: crawler configuration takes a day but on its own lifts nothing, and a site with no citable sentences has nothing to give even with access wide open.
How do you measure it?
There is no ranking table, so you measure by asking. Fix a set of ten or so prompts about your brand and category, run the same prompts on a schedule under the same conditions, and record whether you were cited and which page was chosen. Controlling conditions is the whole game: if you are logged in or a conversation history persists, answers personalise and the data cannot be compared with last week's. Rewording a prompt breaks the series too.
SEO vs AEO vs GEO
SEO | AEO | GEO | |
|---|---|---|---|
Goal | Rank in search results | Be the cited answer | Be a source during generation |
Surface | The results list | AI overviews, answer snippets | Generative AI chat |
Core work | Keywords, links, technical | Definitions, FAQs, structured data | Same, plus crawler access |
Metrics | Impressions, rank, clicks | Snippet share, clicks | Citation rate on fixed prompts |
How to check | Search Console | Inspect results directly | Repeat prompts under fixed conditions |
Shoplive's view
Standing GEO up as its own project usually fails. What worked for us was not opening a new channel but rewriting documents we already had so that a person could read them easily. Questions as subheadings, the definition in the first paragraph, easily-confused concepts split into a table, figures with a source attached — none of that is a special technique for AI. It is what a good document always required. We treat GEO as the result that follows documents which meet those conditions.
Frequently Asked Questions
Q. Do we need to write new content for GEO?
Strengthening existing content with a definition sentence, FAQs and schema resolves much of it. A full rewrite is not the premise.
Q. Should robots.txt explicitly allow AI crawlers?
If you allow everything already, they can reach you. Listing them explicitly signals intent more than it grants access.
Q. Why do competitors get cited and we don't?
Two pages on the same topic can differ sharply in how easy they are to quote. A paragraph that answers the question directly, figures with sources, and a comparison table are easy to lift; a page filled only with marketing copy offers nothing to cite.
Q. What should a GEO report contain?
Citation count alone is hard to interpret. Read the citation rate across your fixed prompts, which page was chosen when you were cited, and whether that page's search impressions moved with it.
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