What is generative engine optimization (GEO)? The definition, the mechanics and what it changes about your content
In short
Generative engine optimization is the practice of getting your content retrieved and quoted inside the answers that ChatGPT, Perplexity, Gemini and Google's AI Overviews write. It is not a ranking trick and it has nothing to do with geo-targeting. The term was formalized in a 2023 academic paper, and the mechanism it describes is unglamorous: an engine breaks a question into sub-questions, retrieves passages, and cites the ones that answer them with evidence. That makes GEO a publishing problem. Someone has to write the complete, sourced answer to every question your buyers ask before they decide, and keep it current. That is the half scaile runs as managed infrastructure, researched from your own knowledge base and approved by your team before anything goes live.
Simon WilhelmCo-founder
Simon is co-founder of scaile and works with enterprise teams on visibility in AI search, from the first audit to defensible Share of Voice numbers.
Key takeaways
GEO is optimization for retrieval and citation inside a generated answer, not for a rank on a results page. The unit of optimization is the passage.
The term was formalized in a November 2023 paper by researchers at Princeton, IIT Delhi, Georgia Tech and the Allen Institute for AI, published at KDD 2024. It is an academic framework, not an agency coinage.
Google states one hard requirement for appearing in its AI features: the page must be indexed and eligible for a snippet. No AI-specific files, markup or schema are required.
Ranking well does not carry over. Only 38 percent of pages cited in Google's AI answers rank in the top 10 for the question asked, and 31 percent rank beyond position 100.
The measurable outputs change: mention rate, citation share and share of answers per question replace position and click-through rate.
GEO is a publishing problem more than a technical one, which is why it stalls at capacity. That is the half scaile takes on, with your team approving every article before it publishes.
Generative engine optimization (GEO) is the practice of making your content likely to be retrieved and cited inside an AI-generated answer. The target is not a position on a results page. It is a mention, a quotation or a linked citation in the response that ChatGPT, Perplexity, Gemini, Copilot or Google’s AI Overviews hands the person who asked.
One disambiguation first, because the abbreviation is overloaded: in advertising and local marketing, “geo” is short for geo-targeting and has nothing to do with any of this. In AI search, GEO stands for generative engine optimization.
The work itself is less exotic than the name suggests. Generative engines answer by retrieving passages and assembling them, so the thing being optimized is a passage, not a page. What makes a passage get picked is that it answers a specific question completely, states its evidence, and is current. Which means most of GEO is not a settings change on your website. It is writing the answers your buyers need and keeping them alive, which is exactly the work scaile runs as managed infrastructure: a named AI Search Strategist, research drawn from your own knowledge base, every fact checked to a source, your team approving before publication, live in 14 days.
What does generative engine optimization actually mean?
It means shaping content so that a generative engine chooses it as a source when it composes an answer.
A traditional search engine returns a ranked list and lets the person choose. A generative engine reads a question, decides what it needs to know, fetches passages from wherever it can, and writes a single response. Your content either makes it into that response or it does not. There is no page two to be on.
The scope is wider than Google. GEO covers every system that answers rather than lists:
- ChatGPT, both from the model’s own knowledge and from live search results
- Perplexity, which is retrieval-first and cites almost everything it uses
- Gemini and Google AI Overviews and AI Mode, which sit on top of the Google index
- Copilot, which sits on Bing
- Assistant surfaces inside tools your buyers already use
Each has its own crawler, its own index and its own citation habits, but the underlying selection problem is the same one, which is why a single body of well-made answers tends to travel across all of them rather than needing a version per engine.
Who invented generative engine optimization?
The term was formalized in an academic paper, not by a vendor. “GEO: Generative Engine Optimization” was submitted in November 2023 by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, with three of the six at Princeton University and the others at IIT Delhi, Georgia Tech and the Allen Institute for AI. It was published at KDD 2024, the ACM’s main data-mining conference.
Two things are worth taking from that. The paper defined generative engines as a category and proposed a way to measure visibility inside them, which is the part that survives. And it reported that structuring content differently could lift visibility in generated responses by up to 40 percent, with the effect varying substantially by domain.
The honest framing is “formalized” rather than “invented”. People had been noticing that AI answers cited some sites and not others before anyone published on it. What the paper contributed was a name, a benchmark and a method, which is why the term stuck rather than one of the dozen alternatives.
How does generative engine optimization work?
It works through retrieval. Understanding the retrieval step is most of what separates GEO from guesswork.
When a generative engine gets a question, it usually does not search for that question. It decomposes it. Google calls this query fan-out and describes it plainly: the system issues multiple related searches across subtopics and data sources, then assembles the response from what comes back. Ask “which GEO agency should a German B2B SaaS company hire”, and the system may separately look for what a GEO agency does, which ones operate in Germany, what they charge, and what B2B SaaS buyers should check before signing.
Three consequences follow, and they are the whole practical core of GEO:
Your page competes for sub-questions, not for the question. A page that answers one of those four sub-questions completely can be cited in an answer to a question it does not rank for at all.
Passages are the unit, not pages. The engine lifts the section that answers the sub-question. A 3,000-word guide where the answer is buried in paragraph nine loses to a 400-word section with the answer in its first sentence.
Evidence is what distinguishes candidates. When several passages say roughly the same thing, the one carrying a named source, a figure or a direct quotation is the safer one for a system that has to stand behind its answer.
There is also a second, slower mechanism: what the model absorbed during training, and what other sites say about you. For brand-related questions, only around 23 percent of citations point back at the brand’s own website. The rest are third parties. Your own site is one input, not the venue.
Is generative engine optimization real, or a buzzword?
Both, and it is worth separating them, because the skepticism is well earned.
The category is real. Google publicly documents query fan-out and the eligibility rules for its AI features. There is a peer-reviewed benchmark. And the demand side has moved: in a March 2026 survey of 1,076 B2B software buyers, G2 found 51 percent now start software research with an AI chatbot more often than with Google, up from 36 percent seven months earlier, and 71 percent use one somewhere in the process. In the same study, 69 percent ended up choosing a different vendor than they had expected to, and 33 percent bought from a brand they had never heard of before an AI recommended it.
What is buzzword is a large part of what gets sold as GEO. Three claims should end a sales conversation:
- “You need an llms.txt file / AI schema / special markup.” Google states there are no additional technical requirements beyond being indexed and snippet-eligible, and explicitly that you do not need new machine-readable files, AI text files, markup, or any special schema.org structured data. Files like
llms.txtare not harmful and no engine currently requires one. - “We can guarantee you appear in ChatGPT.” Nobody controls a model’s output. What can be influenced is whether a good answer exists and is reachable.
- “GEO replaces SEO.” It does not. Without indexing there is no retrieval. GEO sits on top of the same foundation, which the GEO versus SEO comparison works through in detail.
The useful test for any GEO claim is whether it survives the question “what specifically changes on the page, and what evidence says that lever works”. Most do not.
What are examples of generative engine optimization in practice?
The changes are editorial, and they are unglamorous. Four patterns cover most of the real work:
Turning a headline into the question, and the first sentence into the answer. “Pricing” becomes “How much does a GEO programme cost per month?”, and the paragraph under it opens with a figure and a range rather than with context. The section becomes independently quotable, which is the only form in which it can be retrieved.
Adding the evidence into the passage rather than the footer. A claim reading “AI answers reduce clicks” is not retrievable as an answer. “In Germany, AI Overviews appear on around 20 percent of queries, and the click-through rate on position one falls from 27 to 11 percent when they do” is. This is the lever the original GEO paper found to be the strongest, and its effect was largest on pages that were not already ranking near the top.
Answering the sub-questions on the page that owns the topic. If the fan-out for your category reliably produces “what does it cost”, “who are the providers” and “how is it measured”, those need to exist as answers somewhere on your site, cross-linked, with one page owning each.
Refreshing at the same URL instead of republishing. Generative engines favour current sources, and a rebuilt page at a new URL throws away whatever standing the old one had. Update in place.
None of this is a trick. It is the same content operation a good editorial team has always run, aimed at a different selection mechanism.
How is GEO measured, if not by rankings?
By whether you are named, and by whose sources get used instead of yours.
Position and click-through rate stop describing the outcome, because the outcome frequently involves no click. What replaces them:
- Mention rate — across a fixed set of real buying questions, how often does an engine name you at all
- Citation share — when it links sources, how often is one of them yours
- Share of answers — against named competitors, what proportion of the answer space do you hold
- Cited sources — which domains the engine actually reaches for, which tells you where the gap is
The mechanics of this matter more than the dashboard. A check has to run against the questions your buyers ask, not against your brand name, repeatedly, in more than one engine, in a clean session. Asking an assistant about your own company is a leading question and will flatter you, which the free AI visibility check method walks through, along with what a manual check cannot tell you.
The number itself needs a reference point before it means anything. A 20 percent mention rate is strong in some categories and poor in others, which is what a good AI visibility score sets out.
You can see where you currently stand with the free AI Visibility Check. If it comes back near zero while your Google rankings are fine, that combination has a small set of causes, and this diagnosis narrows them down in about half an hour.
What GEO asks of a content operation
The technical half of GEO is roughly a day of work: confirm the AI crawlers can reach you, that nothing is blocked at the CDN, that pages are indexed and snippet-eligible. The AI crawler reference covers which bots matter and what blocking each one costs.
The other half is a publishing programme that does not end. Every buying question needs a complete, sourced answer that stays current, in a category where the facts move quarterly. Most teams have the expertise and not the capacity, which is where programmes stall, and it is the reason the market has filled with agencies and tools. If you are weighing those, the scored agency comparison and what GEO actually costs are the two pages to read first.
scaile is the managed alternative to that retainer. A named AI Search Strategist builds the programme with you, the engine researches from your own knowledge base rather than from the open web, every claim carries its source in an editor built for review, your team approves before anything publishes, and pages are refreshed at the same URL as the field moves. Onboarding to live AI visibility takes 14 days, and Building Radar doubled its qualified inbound leads in 90 days on it. Where the content is AI-assisted, the EU AI Act’s labeling duty is the rule that governs it, and the human-review exemption is built into how every article is produced.
FAQ
What is generative engine optimization in simple terms?
It is the work of making sure that when someone asks an AI assistant a question in your market, your company is what it names and your pages are what it cites. The unit being optimized is a passage that answers a specific question with evidence, not a page competing for a rank.
Is GEO the same as AEO, LLMO or AI SEO?
They overlap almost entirely and the distinctions are mostly marketing. GEO has become the umbrella term, AEO is the older name carried over from the featured-snippet era, LLMO is the more technical variant, and AI SEO is what buyers tend to search for. Anyone claiming they are fundamentally different disciplines is usually selling one of them.
Does GEO replace SEO?
No. A page that is not indexed cannot be retrieved, so the SEO foundation is a precondition rather than an alternative. What changes is where the payoff sits: the ranking stays and the click often disappears.
Do I need an llms.txt file for GEO?
No engine currently requires one. Google states explicitly that no new machine-readable files, AI text files, markup or special structured data are needed to appear in its AI features. An llms.txt file is cheap and harmless, but treating it as the deliverable is how GEO budgets get spent on nothing.
How long does generative engine optimization take to work?
Crawler access changes can show up within days once a page is re-fetched. Content changes move on the publishing and re-indexing cycle, so a realistic first read is a few weeks per question and a few months for a category. Anyone promising results in days is describing a technical fix, not a programme.
Can you do GEO without publishing new content?
Partly. Fixing crawler access, restructuring existing pages so each section answers one question, and adding evidence to claims you already make will move things without a single new article. It runs out quickly, because the questions you have never answered cannot be retrieved from pages that do not discuss them.
Who should own GEO internally?
Whoever owns editorial output, with support from whoever owns the site’s technical health. Filing it under technical SEO alone is the most common way it stalls, because the bottleneck is almost never a setting. It is that nobody has the capacity to write and maintain the answers. scaile exists to take that half, running as the content engine and the strategist alongside whatever SEO work you already have, with your team’s approval on every piece.
Sources
- GEO: Generative Engine Optimization, Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, submitted November 2023, published at KDD 2024. The origin of the term, the GEO-bench benchmark, the up-to-40-percent visibility figure and the finding that effect size varies by domain.
- GEO: Generative Engine Optimization, ACM Digital Library. The KDD 2024 publication record and the authors’ institutional affiliations.
- Google Search Central: AI features and your website. Indexing and snippet eligibility as the only requirement, the statement that no AI-specific files, markup or schema are needed, the preview controls, and the description of query fan-out.
- The Answer Economy: G2’s 2026 AI Search Insight Report, March 2026 survey of 1,076 B2B software buyers. The 51 percent, 71 percent, 69 percent and 33 percent figures on AI-led software research and vendor switching.
- Ahrefs on search rankings and AI citations. The share of cited pages coming from the top 10 and from beyond position 100.
- SISTRIX on AI Overviews in Germany, February 2026, 100 million German keywords. Prevalence of AI Overviews and the fall in click-through rate on position one.
- Omniscient Digital on how LLMs source brand information. The share of citations for brand questions that point at the brand’s own domain.
