Generative engine optimization
Generative engine optimization (GEO) is one of the names given to the practice of structuring digital content and managing online presence to improve visibility in responses generated by generative artificial intelligence (AI) systems.[1][2] The practice influences the way large language models (LLMs), such as ChatGPT, Google Gemini, Claude, and Perplexity AI, retrieve, summarize, and present information in response to user queries.[3][4] Related terms include answer engine optimization (AEO)[4] and artificial intelligence optimization (AIO).[5]
The concept of GEO first appeared in response to generative AI technologies being integrated into mainstream search and information retrieval systems.[3][2]
Tools such as Ahrefs, Otterly.ai, Lumentir, Semrush, BuzzSense.ai, GetMint, Profound, Similarweb, and Writesonic are used to monitor how websites and brands are cited, referenced, or incorporated into responses produced by large language models.[6]
Practitioners also measure how often a brand is mentioned in AI-generated answers, which URLs or domains are cited in those answers, and a brand’s share of voice relative to competitors.[citation needed]
Nick Fox, Vice President of Google Product, has stated that “optimizing for AI search is the same as optimizing for traditional search (SEO)”.[7]
Terminology
[edit]Several overlapping terms describe related practices, and usage varies across practitioners, vendors, and publications. No consensus definition distinguishing these terms had been established in the academic literature as of early 2026, and the terms are frequently used interchangeably in trade and practitioner contexts.[4]
Answer engine optimization (AEO) is sometimes used specifically in reference to systems designed to return direct answers rather than lists of links, such as voice assistants and featured snippet formats, predating the widespread deployment of large language model-based search.[8] Large language model optimization (LLMO) is used in some practitioner contexts with a narrower focus on influencing a model's parametric knowledge rather than on retrieval-based systems.[9] Artificial intelligence optimization (AIO) appears in some vendor and agency contexts as a broader umbrella term covering any practice aimed at improving an entity's representation across AI systems.[10] AI SEO is used when the practice is positioned as a direct continuation of traditional search engine optimization workflows adapted for AI-mediated discovery environments.[4]
See also
[edit]References
[edit]- ^ Aggarwal, Pranjal; Murahari, Vishvak; Rajpurohit, Tanmay; Kalyan, Ashwin; Narasimhan, Karthik; Deshpande, Ameet (2023-11-16). "GEO: Generative Engine Optimization". arXiv.org. doi:10.48550/arXiv.2311.09735. Retrieved 2026-04-10.
- ^ a b Herrman, John (2025-08-04). "SEO Is Dead. Say Hello to GEO". Intelligencer. Retrieved 2025-11-11.
- ^ a b "As AI Use Soars, Companies Shift From SEO To GEO". Forbes. 4 May 2025. Retrieved 28 September 2025.
- ^ a b c d Newman, Nic (12 January 2026). "Journalism, media, and technology trends and predictions 2026". Reuters Institute for the Study of Journalism. University of Oxford. Retrieved 30 January 2026.
- ^ Fan, Zhenan; Ghaddar, Bissan; Wang, Xinglu; Xing, Linzi; Zhang, Yong; Zhou, Zirui (1 July 2026). "Artificial intelligence for optimization: Unleashing the potential of parameter generation, model formulation, and solution methods". European Journal of Operational Research. 332 (1): 1–30. doi:10.1016/j.ejor.2025.08.029. ISSN 0377-2217.
- ^ "Brands target AI chatbots as users switch from Google search". Financial Times.
- ^ "Google exec says AI search optimization is 'the same' as SEO". Martech. Retrieved 6 March 2026.
- ^ "Answer Engine Optimization (AEO): The comprehensive guide for 2026". CXL. 27 January 2026.
- ^ "LLMO and GEO: What We Know About Optimizing for LLMs and AI". Outerbox. 22 August 2025.
- ^ "Large Language Model Optimization (LLMO) explained". Evergreen Media. 12 February 2026.
