What is GEO (generative engine optimization)?
GEO (generative engine optimization) is a new name for an old question: when a customer asks for a recommendation, does your product come up? The difference is who answers. It used to be a list of ten blue links. Now it is often ChatGPT, Perplexity, Claude or Gemini, writing one answer that names three or four products.
This guide defines the terms people use for this work, shows how they differ from SEO, and sums up what published research says actually helps. Every number links to its source. We sell a GEO tool, LazyGEO, so we say where our own product comes in, and we keep it out of the definitions.
What is GEO (generative engine optimization)?
GEO (generative engine optimization) is the work of making your product, brand or page more likely to be named, quoted or linked when an AI assistant writes an answer. It covers ChatGPT, Perplexity, Claude, Gemini and Google’s AI Overviews. The goal is to be inside the answer, not ranked below it.
The term comes from a research paper by Aggarwal and colleagues, presented at KDD 2024, which called these AI systems “generative engines” and tested ways to change how often a page shows up in their answers (GEO paper, Princeton and others).
In practice, GEO means three things:
- Being on the pages AI reads for your category: “best X” lists, comparison pages, review sites, forums.
- Making your own facts easy to read: what you do, who it’s for, what it costs, on pages any crawler can open.
- Checking what AI actually says, again and again, because answers change.
What is AEO (answer engine optimization)?
AEO (answer engine optimization) is the work of making your content the direct answer a system gives to a question. It started before chatbots, with Google’s featured snippets and voice assistants like Alexa and Siri. Today people use it for AI assistants too, so AEO and GEO mostly overlap.
The difference is one of emphasis. AEO grew out of question-and-answer formatting: a clear question as a heading and a short, complete answer right under it. That is still useful, and this guide is written that way. GEO is broader: it also covers whether other sites mention you, which index an assistant searches, and whether the answer names you at all.
What is LLMO (large language model optimization)?
LLMO (large language model optimization) is the work of shaping what large language models say about you. It covers two sources: what a model learned in training, and what it finds when it searches the web live. Many people use LLMO, GEO and “LLM SEO” as near-synonyms for the same job.
The training part is slow and hard to steer: a model learns from a snapshot of the web, and you cannot see or edit that snapshot. The live search part moves faster: when ChatGPT, Perplexity, Claude or Gemini search the web before answering, a page published this month can be in the answer this month. Most practical GEO work targets the live search part.
What is AI visibility?
AI visibility is the measurement side: how often, and how, AI assistants name your product when people ask the questions your customers ask. It answers “are we in the answer?” for each assistant and question. It is a number you track, not a method; GEO, AEO and LLMO are the methods that move it.
A useful AI visibility check records four things for every question: which assistant answered, whether it named you, which competitors it named, and which pages it quoted. The last one matters most, because those pages are where you can act.
How is GEO different from SEO?
SEO (search engine optimization) aims to rank a page high in a list of search results. GEO aims to get your product named inside one written answer. SEO wins a click; GEO wins a mention, often on someone else’s page. They share a base: pages that search engines can crawl, index and trust.
| SEO | AEO | GEO / LLMO | |
|---|---|---|---|
| What you want | A high rank in the results list | To be the one direct answer | To be named or quoted in an AI-written answer |
| Where it happens | Google, Bing results pages | Featured snippets, voice assistants, AI answers | ChatGPT, Perplexity, Claude, Gemini, AI Overviews |
| Main lever | Your own pages and links to them | Clear questions with short answers on your pages | Your facts plus mentions on pages AI already reads |
| Who controls the page AI uses | Mostly you | Mostly you | Often someone else: list authors, review sites, forums |
| How you measure | Rank, clicks, traffic | Snippet wins | AI visibility: which answers name you, quoting which pages |
| How stable results are | Fairly stable week to week | Fairly stable | Changes from one run to the next |
The big shift is the fourth row. In SEO you mostly work on your own site. In GEO, many of the pages that decide the answer belong to other people.
Where do AI assistants get their answers?
When AI assistants search the web, each one leans on a search index: ChatGPT search uses Bing, Claude uses Brave, and Gemini uses Google. Most of the pages they quote are not the product’s own site. They are “best X” lists, review sites, news articles and other third-party pages.
The numbers behind that:
- Search indexes. Profound’s analysis found Claude’s web results line up closely with Brave Search (Profound). Google says pages must be indexed in Google Search to appear in its AI features (Google Search Central), and Gemini Apps ground answers with content from the Google Search index (Google’s crawler docs).
- “Best X” lists. In an Ahrefs study of ChatGPT answers to 750 prompts asking for software, product and agency recommendations, “best X” blog lists were 43.8% of all cited page types, the largest group (Ahrefs).
- Earned media. Muck Rack’s analysis of AI citations found about 84% came from earned media: journalism, research, encyclopedias like Wikipedia and other non-paid sources (Muck Rack, “What Is AI Reading?”).
So if your product is missing from the lists and articles in your category, the assistant has little to quote, however good your own site is.
Why do AI answers change every time you ask?
AI answers are generated fresh each time, so the same question can name different products on every run. SparkToro’s research found the chance of getting the same list of brands twice was below 1 in 100. One answer proves little; you need the same question asked many times, across assistants.
Search Engine Journal’s write-up of the SparkToro study is here. The practical lesson: never judge your AI visibility from one screenshot. Ask each important question several times and look at how often you appear, not whether you appeared once.
What helps a product get named by AI?
Research points to three things: being mentioned across the web, keeping pages fresh, and writing pages with concrete facts, numbers and quotes. None of them is a trick. They make your product easier for an AI to find, trust and quote when it builds an answer.
- Mentions of your brand. Across 75,000 brands, Ahrefs found branded web mentions had the strongest link to appearing in Google’s AI Overviews, a correlation of 0.664, higher than links or domain rating (Ahrefs). A correlation is not proof of cause, but it points the same way as the Muck Rack data.
- Freshness. Ahrefs found pages cited by AI assistants were on average 25.7% fresher than pages in regular search results (Ahrefs). Updating pages with real changes, and showing the date, is worth doing.
- Quotes and statistics. In the GEO paper’s lab tests, adding quotations, statistics and cited sources raised a page’s visibility in generated answers by up to about 40% (GEO paper). That was a research benchmark, not live ChatGPT, so treat it as a direction, not a promise.
Does llms.txt help you get recommended?
There is no evidence that it does yet. An analysis of about 300,000 domains, reported by Search Engine Journal, found no clear link between having an llms.txt file and being cited by AI. It is cheap to publish, so it does no harm, but it is not a GEO strategy.
The study is summarized here. We publish one at lazygeo.com/llms.txt because it takes minutes and keeps our facts in one place, not because we expect it to move answers.
How do you measure GEO?
Write down the questions your customers ask before they choose a tool. Ask each one to ChatGPT, Perplexity, Claude and Gemini, several times. Record who gets named and which pages get quoted. Repeat every week. The quoted pages are your to-do list; the trend over weeks is your score.
You can do this by hand in a spreadsheet. Our own first run of this check, on LazyGEO itself, found 0 of 81 answers named us; that post shows the method and what we changed.
Where does LazyGEO fit?
LazyGEO is GEO software for startups: an AI team that runs the weekly check above, writes comparison pages on your own domain, and writes to the authors of lists AI quotes. Nothing goes out in your name without a 24-hour window and a Stop button. You can do all of this yourself too.
If you want to see what AI can read on your site today, the free check is free and needs no signup. To see how LazyGEO compares with tools like Semrush, Peec or Otterly, read the comparisons. For the next step, read GEO vs AEO vs SEO for a SaaS founder or how to get recommended by Perplexity, Claude and Gemini.