What is generative engine optimization (GEO)?
Generative engine optimization (GEO) is the practice of improving how visible your content and brand are in answers that AI systems write, such as those from ChatGPT or Google’s AI Overviews. This page is written by Dmitry (Dee) Kargaev, who maintains Awesome GEO and wrote the SEO-to-GEO Gap and SEEN working papers.
Where the term GEO comes from and what it is called
The term was introduced in “GEO: Generative Engine Optimization”, a paper by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, published on 24 August 2024 in the proceedings of the KDD ‘24 conference. The paper formalizes generative engines as search engines that use generative models to gather and summarize information. It proposes GEO as a black-box optimization framework that helps content creators improve their visibility in those engines’ responses. Its authors also introduce GEO-bench, a benchmark of diverse user queries across multiple domains.
Industry guides give their own definitions. SEOS.co’s guide, updated 11 August 2026, calls GEO the practice of making a brand and its information easier for AI search systems to find, understand, trust, cite and recommend. Conductor’s academy page, a vendor page last updated 4 November 2025, defines it as creating and refining website content so that answer engines and AI chatbots can understand, surface and present it.
Ampere’s guide, a vendor explainer from 15 October 2026, says GEO, AEO, LLMO and AI SEO are one family of terms with different emphases, and that none of them has a standard definition. Conductor’s page says AEO and GEO deal with the same core concept.
What GEO work consists of
The SEEN working paper (SSRN, 2026) sorts GEO work into four layers: Structure, Evidence, Entity and Notability. It asks whether an entity is easy for AI systems to find, read, verify, resolve and recommend. The paper treats AI visibility as a corpus-engineering problem: owned content, platform-hosted first-party content and third-party corroboration must read as one coherent, verifiable entity. Its starting point is that traditional SEO treats the ranked page as the unit of work, and that generative search weakens that model because it retrieves passages and synthesizes answers without a stable ranked list.
| Layer | What the work is |
|---|---|
| Structure | Definitions, pricing, scope, comparisons and core facts sit in visible HTML with real hierarchy, so the answer can be extracted. |
| Evidence | Numbers, outcomes and positioning are paired with methodology pages, case studies, bylines and primary sources, so claims can be verified. |
| Entity | The entity has one canonical name, one description, consistent profiles, schema and a clean sameAs graph, so it is unambiguous. |
| Notability | Third-party mentions, platform-hosted reach, links and organic community presence corroborate the recommendation. |
The paper also contributes a 41-item practitioner checklist: 10 items for Structure, 10 for Evidence, 10 for Entity and 11 for Notability. The checklist is available as a PDF on the SEEN site.
Other guides sort the work differently. impik.dev’s wiki, a web studio’s entry updated 1 September 2026, starts with the search foundation of crawl access, indexability, internal links and site architecture. It then lists a direct answer near the heading, separate sections for separate questions, primary sources for claims, unambiguous names, important information as text rather than images, and structured data that agrees with the visible page.
GEO still runs on top of organic search
The SEO-to-GEO Gap working paper (Kargaev, 2026) reports as a provisional finding that the overlap literature indicates GEO still operates on top of organic search rather than replacing SEO outright. The paper is an exploratory comparative synthesis of direct SEO and GEO studies, with a second layer of market, overlap and citation-behavior sources. It argues for a reweighting model rather than a replacement model: familiar factor families persist, new generative-facing signals emerge, and some headline claims about SEO-to-GEO disruption are stronger than the evidence supports.
Ampere’s guide says GEO builds on SEO because AI answers still require pages to be crawlable and indexed. The impik.dev wiki reports that Google states its normal search requirements apply to AI Overviews and AI Mode, with no special markup required.
What is known to move results
This page relies on two research papers for what moves visibility in AI answers. One is a conference benchmark study and the other is a provisional working paper.
| Finding | Source | Kind of evidence |
|---|---|---|
| GEO methods can boost visibility by up to 40% in generative engine responses, and their efficacy varies across domains | GEO: Generative Engine Optimization (Aggarwal et al., KDD 2024) | Benchmark study (GEO-bench) in conference proceedings |
| Adding citations and statistics produced substantial gains in the GEO benchmark | The SEO-to-GEO Gap (Kargaev, 2026) | Working paper, provisional finding from an exploratory synthesis |
| Visibility in generative search appears more tightly associated with entity and brand prominence than with classic link-proxy measures | The SEO-to-GEO Gap (Kargaev, 2026) | Working paper, provisional finding |
| Authority appears broadly persistent across SEO and GEO | The SEO-to-GEO Gap (Kargaev, 2026) | Working paper, provisional Divergence Index |
What is not known
No source on this page promises a result from GEO work. The SEEN paper does not claim that its framework guarantees citations, recommendations, rankings or traffic, and it sets out a validation agenda for future empirical work. The findings of the SEO-to-GEO Gap paper are provisional. That paper also says the divergence in content quality depends heavily on how the SEO-side measure is mapped.
Ampere’s guide reads the KDD 2024 study as one rewritten page measured against unchanged competitors. On that reading, its results show which kinds of changes engines reward, not how much your own pages will gain.
The 2026 impik.dev wiki says GEO has no universal standard or fixed checklist. It adds that a method seen on one platform or query set cannot be assumed to work on every generative system, and that an llms.txt file cannot guarantee a mention or citation. It also says there is no single GEO metric, and that one appearance is not stable visibility because an answer may change when the same question is asked again. SEOS.co’s 2026 guide says platform behavior differs, so a page visible in Google AI Overviews may not receive the same treatment in ChatGPT or Copilot. It also says no schema type or isolated tactic guarantees inclusion in an AI-generated answer.