Generative engine optimization

Generative engine optimization (GEO) is the practice of adapting digital content and online presence management to improve visibility in results produced by generative artificial intelligence (GenAI). The term was first introduced in November 2023 by six researchers in an academic paper.^[1] GEO describes strategies intended to influence the way large language models, such as ChatGPT, Google Gemini, Claude, and Perplexity, retrieve, summarize, and present information in response to user queries.^[2]

Unlike traditional search engine optimization (SEO), which focuses on improving rankings in conventional search engines such as Google or Bing, GEO specifically targets generative engines — AI-driven systems that produce direct, summarized answers rather than lists of external links.^[3] The approach aims to ensure that brands and publishers are cited or represented on such platforms. Other terms used to describe the same concept include AI SEO (artificial intelligence search engine optimization) and LLMO (large language model optimization).^[4][5]

History

Origin of the term

The concept of GEO developed in parallel with the rise of generative AI technologies that became integrated into mainstream search and information retrieval systems.^[6] In November 2023, six researchers introduced the term "generative engine optimization" in their paper GEO: Generative Engine Optimization.^[1] They described GEO as "a new paradigm that helps content creators improve the visibility of their content in answers generated by generative engines," stressing the need to adapt existing optimization strategies to the AI-driven search environment.^[1]

In the same study, the researchers introduced GEO-Bench, a benchmark dataset of 10,000 queries designed to evaluate GEO techniques empirically. The results showed that certain optimization practices significantly increased the likelihood of a source being cited or included in generative engine answers, recognizing GEO as a distinct though related field to SEO.^[1]

Adoption and industry growth

Following publication of the paper, the term GEO gained traction among digital marketing firms, SEO consultancies, and technology companies. By early 2024, marketing outlets such as Search Engine Land began covering the concept, identifying GEO as a strategic necessity for visibility in AI-generated content.^[4]

The growing interest led to the development of dedicated GEO tools and services, including getSAO; Cognizo AI; Hall; Scrunch AI; Am I On AI; Peec AI; Otterly AI; Rankshift; Senso; Whitebox;^[7] Profound; and Wilgot. By 2025, generative engine optimization had become a standard part of digital marketing strategies, with many firms incorporating GEO into their SEO workflows.^[8]

Transition to generative engines

Traditional SEO emphasizes keyword density, backlinks, and page rankings within link-based indices. By contrast, generative engines such as ChatGPT, Perplexity, and Google’s Search Generative Experience (SGE) generate direct, context-driven answers to user queries.^[1]

A 2025 study by Apoorav Sharma and Prabhjot Dhiman, titled The Impact of AI-Powered Search on SEO: The Emergence of Answer Engine Optimization, argued that generative AI is transforming the logic of search engines — from a link-based model to a context-based model that delivers immediate, click-free responses. This shift, the authors claimed, alters how digital visibility is measured and achieved.^[9]

Two main categories of AI-powered platforms are identified:

1. Traditional search engines with generative components – Google Search and Bing integrate AI-generated overviews (e.g., Google’s AI Overview) alongside conventional search results, continuing to display SERPs while adding summaries at the top.
2. Dedicated generative engines – Platforms such as ChatGPT, Gemini, and Perplexity operate as answer engines, returning a single synthesized response generated by large language models (LLMs) instead of a list of links.

GEO primarily focuses on the latter category, with the aim of improving the chances that brands and content sources appear in AI-generated answers.^[10][11]

Metrics and measurement

The move to AI-driven platforms changes both optimization methods and the benchmarks for digital marketing success. Traditional measures such as click-through rate (CTR) and first-page ranking are being replaced by new indicators, including:

• Generative appearance score: the frequency and prominence of a source within AI-generated responses.
• Share of AI voice: the proportion of AI answers in which a brand is mentioned.
• AI citation tracking: monitoring mentions and references within AI-generated text.^[12]

Marketing companies have developed dashboards to measure GEO outcomes, adapting key performance indicators (KPIs) to a search environment where visibility begins within an AI-generated conversation rather than on a search results page.^[10]

See also

• Information retrieval