What is Generative Engine Optimization (GEO)? A complete guide

Generative Engine Optimization (GEO) is the process by which we build, publish, and improve information so that generative engines can discover, understand, use, cite, and represent it correctly in responses. The process must be followed by repeatable measurement; otherwise, GEO remains a collection of assumptions about an opaque system.

GEO does not replace SEO. It does not guarantee an appearance in ChatGPT, Gemini, Claude, Perplexity or AI Overviews. Nor can it be reduced to short paragraphs, FAQs, schema markup or an llms.txt file.

It is an emerging discipline at the intersection of information retrieval, SEO, content, reputation, structured data, public relations and the measurement of probabilistic systems.

Where the term GEO comes from

The term was formalized in the paper GEO: Generative Engine Optimization, originally published as a preprint in 2023 and later at the ACM KDD 2024 conference. The authors described GEO as a black-box optimization framework through which creators can attempt to increase the visibility of content in generative engine responses.

The paper also introduced GEO-bench, a set of 10,000 queries, and reported visibility improvements of up to 40% for certain strategies and domains within the experimental framework.

The phrase "up to 40%" is often taken out of context. It doesn't mean that adding a few stats to an article will increase organic impressions by 40% in all AI engines. The result belongs to the protocol, data set, metrics and conditions of that study.

The paper demonstrated that the way information is presented can alter visibility in an experimental setting. It did not demonstrate a universal ranking recipe for ChatGPT.

How GEO differs from traditional search

A traditional search engine mainly displays a list of results. A generative engine can search, select documents, synthesize statements, compare options, and produce a unique answer where sources appear in different positions and forms.

For a publisher or a brand, visibility is no longer a single position. There are multiple thresholds:

1. eligibility — the page can be accessed and used;
2. discovery — the system or its provider finds the URL;
3. retrieval — the document enters the analyzed set;
4. use — the information influences the response;
5. citation — the source is displayed or attributed;
6. mention — the brand or product appears;
7. recommendation — the entity is proposed as a choice;
8. perception — specific claims are associated with it;
9. outcome — the user visits, enquires or buys.

A page can be found without being cited. The brand may be mentioned without being recommended. A citation can bring authority without producing traffic. GEO must keep these events separate.

SEO vs AEO vs GEO

SEO tracks accessibility, understandability, relevance and performance of content in search engines. It includes the technical side, information architecture, content, links, entities and user experience.

AEO, or Answer Engine Optimization, emphasizes the ability of content to clearly answer questions and be used in surfaces that provide direct answers.

GEO extends the problem to systems that generate synthetic responses, use multiple sources, and can represent, compare, or recommend entities in a conversation.

The boundaries are not fixed. In its official guide to generative features in Search, Google says that, from its perspective, optimisation for AI Overviews and AI Mode is still SEO. This is an important clarification: we do not need a new industry of tricks that simply repackages crawlability, useful content or internal linking.

However, the term GEO remains useful for cross-engine work and issues that a classic SEO report doesn't capture: conversational referrals, citations, perceptions, factual accuracy, and variation across models.

The foundation of GEO is still technical

An excellent but inaccessible article cannot normally compete for retrieval. Before attempting sophisticated optimisation, check:

• HTTP status and URL stability;
• robots.txt and meta directives;
• indexing and snippet eligibility;
• content available in HTML;
• canonical and hreflang;
• internal linking and sitemap;
• page speed and user experience;
• match between structured data and visible text.

Google states that no special schema or dedicated AI file is required to appear in its generative features. More directly, Google says that it does not use llms.txt for Search and that the file neither helps nor affects ranking in Google.

For ChatGPT Search, OpenAI recommends enabling OAI-SearchBot so that pages can be discovered, featured and cited. GPTBot, used for access control in the context of training, has a different role. Confusing them can produce wrong robots rules.

I have detailed the checks in the article Your site looks good. But can Google, ChatGPT and AI agents read it?.

Clarity of entities and claims

A brand is not just a repeated name. It is an entity related to products, services, founders, locations, industries, customers, evidence and verifiable claims.

If the site describes the same product inconsistently, uses unstable names, or keeps old prices and features, an AI system may pick up the wrong version. GEO therefore includes information hygiene:

• a canonical page for the entity;
• consistent names and aliases;
• atomic assertions that can be checked;
• sources and update dates;
• separation of available features from roadmap items;
• correction of outdated information.

This is why a brand knowledge base can be operationally useful: not to “train the Internet” on demand, but to maintain a source of truth against which we can check both what we publish and what the engines say.

GEO content must be worth using

Formatting helps readers and extraction systems, but it cannot compensate for missing information. An article that merely restates general definitions remains generic content, even if it has 30 subheadings and FAQ schema.

Content worth using usually has one or more of these qualities:

• answers a real question directly;
• defines terms and limits;
• offers proprietary data, methodology or experience;
• cites primary sources;
• compares options using declared criteria;
• shows steps, risks and exceptions;
• is current and has an identifiable author;
• can be checked independently.

Google explicitly recommends unique, valuable, non-commodity content and warns against inauthentic mentions. This contradicts two popular "fast GEO" tactics: mass publishing nearly identical pages and buying mentions with no editorial value.

Query fan-out changes architecture, not just keyword research

Some systems can break down a complex question into sub-searches about criteria, alternatives, risks and sources. Google officially calls this technique query fan-out.

The implication is not to publish a page for every imaginable variant. It is to build a coherent system of pages covering the decision: definition, comparison, evidence, implementation, limitations and examples.

I explain the mechanism and its pitfalls in the guide Query fan-out: the hidden queries that determine whether you appear in AI answers.

Third-party sources and reputation

Your own site is necessary, but not the only source. Press, documentation, directories, reviews, communities, partners, and databases can influence what information is available about an entity.

A responsible strategy is not about collecting as many mentions as possible. It pursues relevant, independent and accurate sources. A fabricated review or an article published solely to repeat a commercial message can create more risk than authority.

GEO meets digital PR, reputation and off-page SEO here, but the goal must remain verifiable information, not clandestine manipulation of an answer.

How we measure GEO

A GEO intervention without baseline and remeasurement is just an editorial change. For evaluation we need:

1. a stable set of themes, personas and scenarios;
2. declared engine, interface, language and geography;
3. repeats and sampling rules;
4. separate metrics for visibility, recommendation, citation and accuracy;
5. raw responses and versioned extraction;
6. coverage, errors and confidence intervals;
7. an accurate record of intervention;
8. a comparable post-publication cohort;
9. conclusions that do not confuse correlation with causation.

In the guide What is visibility in AI and how to measure it correctly I explain the formula, the denominators and the difference between visibility rate, recommendation rate and recommendation coverage.

What's not yet demonstrated

A critical survey published as a preprint in July 2026 shows that GEO terminology and metrics remain heterogeneous. The author disentangles discovery, citation, information use, and economic outcomes and finds that evidence for organic, stable, longitudinal, and cross-platform effects is still limited.

That doesn't mean GEO is useless. It means we have to be precise:

• we can see occurrences and citations;
• we can test how a response changes in a protocol;
• we can improve the accessibility and clarity of information;
• we can compare cohorts before and after;
• we cannot promise control of an external model;
• we cannot automatically assign an increment to a single change.

Seven myths about GEO

1. "You just need to create llms.txt." Not for Google Search; other services may use the file, but it's not a guarantee.
2. “Schema makes you citable.” Schema can aid understanding when it matches the visible text, but it does not force a system to cite the page.
3. “FAQs rank in ChatGPT.” Genuine questions can help the reader; fabricated FAQs are not a strategy.
4. "More mentions mean more recommendations." A mention can be neutral or negative.
5. “A successful test proves optimisation.” A screenshot proves only that one response existed.
6. “GEO replaces SEO.” The technical and editorial foundation remains SEO.
7. "There is an identical formula for all engines." Systems, interfaces and sources differ.

A GEO plan for the first 90 days

Days 1–30: measure and clean

• define the entity, aliases and competitors;
• build real scenarios, not only branded prompts;
• set the baseline and methodology;
• check technical access, indexing and conflicting content;
• inventory sources and inaccurate claims.

Days 31–60: publish evidence

• strengthen canonical pages;
• fill the information gaps;
• publish data, methodology, comparisons and examples;
• link the product, documentation, author and evidence coherently;
• correct third-party sources where a legitimate correction process exists.

Days 61–90: reassess and decide

• repeat the comparable cohort;
• verify visibility, recommendations, citations and accuracy;
• record changes in the model and coverage;
• preserve useful interventions;
• revise or remove what does not produce credible signals.

The AYSA model: from observation to intervention

The thesis on which I am building AYSA is: measure → explain → approve → execute → remeasure.

The important distinction is not another score. It is the controlled link between observation and action: we identify what is missing, show the evidence, propose a change through an established workflow, request approval and measure again under comparable conditions.

This is product direction and editorial methodology. It does not mean that all the engines, integrations and interventions described are already available. The AYSA documentation will mark each capability as available, beta, planned, or methodology only.

Verdict

GEO is a real problem, but a still immature industry. Generative engines are changing the way information is found, synthesized and recommended, and brands need a method to observe and improve this representation.

The serious approach is not to chase every “hack”. It is to build accessible, clear, original and verifiable information, connect it with credible sources, measure behaviour across repeatable cohorts and then apply controlled interventions.

SEO remains the foundation. GEO adds the question that the old reports couldn't fully answer: how is the brand used and represented after the engine no longer just links but composes the answer?

Sources

• GEO: Generative Engine Optimization — KDD 2024
• ACM Digital Library — DOI of work GEO
• Google Search Central — Optimizing for generative AI features
• Google Search Central — AI features and your website
• OpenAI — Publishers and Developers FAQ
• Optimizing Visibility in Generative Engines: A Critical Survey, 2023–2026 — preprint

Sources last checked: 31 July 2026. The article separates the platforms' official recommendations, academic evidence and the proposed AYSA methodology.