Generative engine optimization
Generative engine optimization is the practice of adapting digital content and online presence management to improve visibility in results produced by generative artificial intelligence. Researchers from IIT Delhi, Princeton University, Georgia Tech, and the Allen Institute for AI introduced the term in an academic paper published in November 2023. GEO describes strategies intended to influence the way large language models, such as ChatGPT, Google Gemini, Claude, and Perplexity AI, retrieve, summarize, and present information in response to user queries. Other terms used to describe similar but different practices include AI SEO and LLMO.
History
Rationale for Emergence
The development of GEO is rooted in fundamental shifts in user behavior, technology, and business analytics that accelerated in the early 2020s.A key factor in this shift has been the adoption of retrieval-augmented generation architectures by generative search systems, in which external documents are indexed, embedded, and retrieved as semantically relevant text segments to support AI-generated responses. This has redirected optimization efforts away from page-level ranking toward the structuring, authority, and retrievability of content within vector-based knowledge repositories used by large language models.
Origin of the term
The concept of GEO developed in parallel with the rise of generative AI technologies integrated into mainstream search and information retrieval systems.Adoption and industry growth
By the mid-2020s, GEO had been incorporated into the service offerings of marketing technology vendors and enterprise analytics platforms that monitor brand representation in AI-generated answers. Examples include tools developed by companies such as Bluefish AI and Semrush, which focus on measuring how brands are cited, summarized, or positioned within responses generated by large language models.In addition to analytics platforms, practitioner-oriented publications have discussed premium editorial placements and digital PR as authority signals within modern AI-mediated search environments, noting that coverage from credible third-party outlets may increase the likelihood of being cited in AI-generated responses.
Industry adoption of GEO has accelerated as practitioners recognize key requirements for visibility in generative AI responses. Primary factors include E-E-A-T signals, which demonstrate expertise, experience, authoritativeness, and trustworthiness through structured content, external citations, and established authority in topical domains. Additionally, content must be retrievable by RAG systems, requiring clear semantic structure, topical depth, and strategic placement of claims within longer-form content that AI systems can extract and synthesize. Academic research auditing generative AI search engines has found that such systems draw heavily from news and media sources, with citation patterns exhibiting commercial and geographic bias.
Practitioner-oriented publications have also discussed Generative Engine Optimization as a multi-layered approach focused on answer-oriented content structure, consistent entity representation, and the reinforcement of authority signals across authoritative sources to support inclusion within AI-generated responses.