What is GEO (generative engine optimization), and how does it work?

GEO is the practice of working on a brand’s website, content and presence on other sites so that generative AI engines mention it when someone asks about its category, describe it with the right attributes and link to its sources. People look for it under several names: “GEO SEO”, “generative engine optimization”, “AI search optimization”, “LLM optimization”. They all point to the same thing. One common mix-up: “geo” also shows up in geotargeting and local SEO, which is a different discipline.

The name comes from an academic paper: “GEO: Generative Engine Optimization”, by Pranjal Aggarwal and five co-authors, posted on arXiv in November 2023 and presented at the KDD 2024 conference. The authors built a benchmark of queries across different topics (GEO-bench) and measured that certain changes to a source’s content, such as adding citations, statistics and references, could increase its visibility in generated answers by up to 40%, with results that varied by topic.

Whatever you call it, the question behind it is usually practical: how do I get my brand to appear in ChatGPT?

SEO aims to get your page to the top of a list of results and earn the click. GEO aims to get your brand inside an answer the AI writes, which often names three or four options and settles the question without anyone clicking. The technical foundation is the same (crawlable, indexed, useful pages), and Google says so plainly in its guide to generative AI features: from Google Search’s perspective, optimizing for generative AI search is still SEO. What changes is the outcome you measure: ranking doesn’t guarantee you’re named, and being named doesn’t tell you how you’re described. The full comparison, AEO included, is in AEO vs SEO vs GEO.

An AI engine answers in two ways, and almost all of them combine both. Without searching, it answers from what the model learned during training: what the web said about your brand up to a cutoff date. With search, it sends queries to a web index, reads pages and writes an answer from what it found, usually with links to the sources. ChatGPT decides in each conversation whether to search; Google’s AI Overviews always start from Google’s index.

When it searches, the engine doesn’t run a single query. Google describes a technique called query fan-out: the model generates a set of related, simultaneous queries to gather more information and answer better. Asked “which digital bank is best for getting paid in US dollars in Argentina?”, it may search separately for fees, limits, account requirements and reviews. Each sub-question is a separate chance to be cited, which is why a brand can be missing from the answer even when it ranks well for the full phrase.

Language counts too: what’s published about your brand in other languages can come into play when an engine searches the web.

These are the levers with evidence behind them, in the order worth checking:

• Indexable pages. To appear in Google’s AI features, a page has to be indexed and eligible to be shown with a snippet; it’s the only requirement Google states. The same applies to engines that search other indexes: if they can’t find you, they can’t cite you.
• Answers to the sub-questions. Price in local currency, coverage by country or city, requirements, timelines, who it’s for and who it isn’t for, and honest comparisons with alternatives. These are the pieces query fan-out goes looking for.
• Citations earned on other sites. In Refractia’s data, a brand’s own website accounts for between 0.5% and 2.8% of what AI cites when it talks about that brand. The rest is media, comparison pages, reviews, directories, forums and industry sites. Google warns that chasing inauthentic mentions doesn’t help as much as it may seem: this is about earned coverage.
• A clear entity. The same name, category and facts on your site, your profiles, your Google Business Profile and, where it applies, Wikidata. If your name can be confused with something else, the engine needs signals to tell you apart.
• Your language and your country. Answers change from one market to another. If your content doesn’t say where you operate, in what currency and with what coverage, you’re competing for a generic answer that isn’t the one your customers see.
• Technical access. AI crawlers have to be able to reach your pages: a robots.txt that allows them isn’t enough if your CDN or firewall blocks them. We cover this in LLM SEO.

Much of what gets sold as GEO has no evidence behind it. Google is clear about it in the same guide: you don’t need to create machine-readable files, AI text files, special markup or Markdown to appear in its AI features, because Google Search doesn’t use them. That includes llms.txt. Nor do you need to break content into tiny chunks or write in a special way for AI.

• One page for every variant of a question. When the goal is to manipulate rankings, Google treats it as scaled content abuse.
• Hidden text aimed at AI. Instructions tucked into the page so the model recommends you. Hidden text was spam for Google long before AI.
• Bought reviews and mentions. They are the fake version of earned citations, with a high reputational cost if they come to light.
• Guaranteed positions. Nobody controls what a model answers; be wary of anyone who guarantees you the top spot in ChatGPT.

Measuring GEO means asking the engines what your customers ask and reading the answers with a method. Search Console doesn’t tell you what the answer says about your brand, or whom it recommends instead of you. Six rules for the number to mean something:

1. The engines your customers use. ChatGPT has 900 million weekly users (OpenAI, February 2026), AI Overviews appear above Google’s results, and Meta AI sits inside WhatsApp and Instagram.
2. Questions in the market’s language that name the country. For customers in Argentina, that means asking in Spanish, “¿Cuál es la mejor nafta en Argentina?”, not “best fuel brands”. A question without a country gets a generic answer.
3. Category questions, not just brand questions. Asking about your name measures how you’re described; asking about the category measures whether you’re named when nobody named you first.
4. Repetition. Answers change from one run to the next. As Natzir Turrado explains in his critique of prompt trackers (in Spanish), model answers are probabilistic and personalized, and a small sample produces rankings that look precise and are noise.
5. Every rate with its sample (“k of n”). “40% visibility” can’t tell 2 answers out of 5 from 400 out of 1,000. And an engine that didn’t answer said nothing: counting it as “answered and didn’t name you” invents a zero.
6. Chat and search, separately. Mixing them averages two opposite behaviors and hides where the problem is.

That’s how Refractia measures. The Prompts Library stores every question, written in the market’s language with the country named in full, and the answer from ChatGPT, Claude, Gemini, Google’s AI Overviews (queried with each country’s location) and Meta AI. Share of Voice compares you with your competitors, AI Perception shows each engine’s sentiment and the adjectives it uses, Performance by Topic separates where you win and where you disappear inside your category, and Influence Sources lists the domains the engines cite. Two limits: we don’t measure Perplexity, Copilot or Grok, and a person starts each scan; it’s an in-depth diagnostic, not daily monitoring.

A plan for a marketing team starting from zero, with no paid tools:

1. 01

   Days 1 to 3: write your questions

   Write 20 to 50 real questions from your customers, in their language and naming the country: category questions (“what’s the best health plan for a family in Córdoba, Argentina?”), comparisons and purchase intent. Pull them from your sales team, Search Console and customer support.

2. 02

   Days 4 to 7: measure your baseline

   Ask each question in the engines that matter, in sessions with no history, several times. Note how many answers include you (“k of n”), whom they name instead, how they describe you and which sites they cite: that is your source map.

3. 03

   Days 8 to 12: check the technical foundation

   Confirm that your key pages are indexed in Google Search Console and Bing Webmaster Tools, that neither robots.txt nor your CDN blocks AI crawlers, and that prices and products are in the HTML the server returns, not loaded with JavaScript.

4. 04

   Days 13 to 20: cover the sub-questions you’re losing

   For each question where you don’t appear, identify what the engine was missing: price, coverage, requirements, a comparison. Write or fix the page that answers it, with the answer in the first sentence.

5. 05

   Days 21 to 27: sort out your entity and pick your sources

   Unify your name, description and facts across your site and your profiles. From your source map, choose three third-party sites the engines already cite in your category and work to be on them with real information.

6. 06

   Days 28 to 30: measure again

   Repeat exactly the same questions and compare. What depends on web search can move within weeks if your pages get indexed; what depends on the model’s knowledge changes more slowly, with new versions.