What is generative engine optimization?

What generative engine optimization means

Generative engine optimization, or GEO, is the work of making AI engines mention your brand, describe it correctly and link to your pages when someone asks a question your product answers. A generative engine is a chat-style search tool such as ChatGPT, Perplexity, Gemini, Grok or Claude that writes one answer instead of returning a list of links.

The name comes from a 2023 paper by researchers at Princeton, published as GEO: Generative Engine Optimization and later accepted to KDD 2024. The paper formalised the problem content creators face: engines synthesise an answer from many sources at once, and the creator has little say in when and how their content shows up. It also benchmarked optimisation tactics and reported visibility gains of up to 40% for the best ones on its test set. Read that as a research result rather than a promise. The paper found the tactics worked differently from domain to domain, which is its own conclusion.

Two outcomes decide whether GEO work is succeeding. A mention is your brand named inside the answer text. A citation is a link to a page the answer drew from. They move independently: an answer can cite your documentation while describing a competitor, or praise you without linking to a single page you own.

How a generative engine assembles an answer

Every answer is built in two steps. Retrieval turns the prompt into searches and pulls candidate passages from the web or an index. Generation is a language model writing the answer from those passages plus what it absorbed during training.

Engines rarely search for the sentence they were given. They rewrite it into several shorter searches, a behaviour called query fan-out, merge the results and keep a shortlist of passages. Those passages are the raw material. If a product comparison never made it into them, the answer has nothing to quote about you, however good your site is.

The third input is what the model already knows: brand facts and category facts learned in training, which are rarely updated and invisible from outside. Engines lean on that memory when retrieval returns little, which is why a well-known brand can get named even in a thin answer. To see which material feeds answers in practice, read which sources AI engines cite.

What GEO work actually consists of

Four jobs: choose the prompts your buyers ask, make your pages retrievable and quotable, get the sites you do not own to describe you accurately, and measure the answers so you know whether any of it worked.

1. Prompt selection. Write down the questions a buyer types before purchasing: category questions, comparison questions, “best X for Y” questions and a few about your brand directly. Fifteen to twenty-five good prompts beat a long list you never read.
2. Page work. Give each important question a page that answers it in the first 40 to 60 words, with headings that state the question and tables where comparisons live. Keep the pages crawlable.
3. Third-party sources. Reviews, comparisons, forum threads and directory listings written by other people. Engines read them as corroboration, and you influence them by correcting stale listings, supplying accurate material and answering the people who ask.
4. Measurement. Run the prompt set on a schedule, keep every original answer, and parse mentions, position, sentiment and citations out of each one. Without this step you are guessing which fix moved what.

The last job is the whole product here at advisible. The setup wizard suggests prompts for your category, each prompt runs daily on ChatGPT, Perplexity, Gemini, Grok and Claude, and every original answer lands in the answer archive so you can read the exact text you are trying to change.

How GEO differs from SEO

SEO competes for a position in a ranked list of links. GEO competes for a sentence inside one written answer. The buyer is the same and most of the groundwork carries over; the surface, the stability and the payoff differ.

What carries over without changes: crawlability, pages that answer a question completely, clean structure, consistent naming of your product and category, and third-party proof. What is new is prompt selection instead of keyword research, variance you can only average out by repeating, and sources chosen at answer time on domains you do not control. The full side-by-side is in GEO vs SEO.

What you can measure, and what you cannot

You can measure what the answers do: whether you appear, how high you appear, how they talk about you and which sources they cite. You cannot measure the code that picks those sources, because no engine publishes it.

• Visibility: the share of answers that mention your brand, where prompts you mark as more important count for more.
• Average position: how high your brand appears in the lists AI gives, on average. 1 is the top, so lower is better.
• Sentiment: how positively answers talk about your brand, from 0 to 100, where 50 is neutral.
• Share of voice: your brand's mentions as a share of all tracked brands and competitors in the same answers.
• Citation share: how often your domains appear among the sources answers link to, over all citations in those answers.

The common mistake: adding JSON-LD and calling it GEO

Structured data helps a machine understand what your page is. It does not get the page into the retrieved set, and it does not make an answer recommend you.

JSON-LD and schema markup are worth doing. They state your product name, category, pricing and reviews in a format machines parse cleanly, and they remove ambiguity about what is true about you. The mistake is treating them as the whole job. Retrieval rewards pages that answer a question, and recommendations follow what the retrieved sources say, most of which you do not publish.

The failure sequence is familiar: add schema markup, tick the box, report GEO as done, never look at an answer. Nothing in that loop checks whether engines mention you at all. The order that works is measure first, read which prompts skip you, fix the page or listing causing it, then re-run and compare windows. If you want the measurement without a budget, start with free ways to track AI visibility.