Generative engine optimization
Generative engine optimization (GEO) is the process of improving the visibility, relevance, and presentation of content in response to queries made to generative engines, such as large language models (LLMs).[1]
Unlike SEO, which targets web search rankings, GEO focuses on how information is surfaced, summarized, cited, or directly generated by artificial intelligence systems powered by generative models.[2]
GEO involves techniques that help content creators, publishers, and organizations shape how their data, branding, and narratives are represented in AI-generated responses. According to *On Marketing*, "Generative Engine Optimization (GEO) is the practice of tailoring content and structure to maximize visibility and usefulness within AI-generated search and recommendation results."[3]
As generative AI becomes increasingly integrated into consumer tools, productivity software, search interfaces, and digital assistants, optimizing for these systems has become a growing concern for marketers, educators, and developers.[4]
See also
- Large language model
- Artificial intelligence
- Prompt engineering
- Machine learning
- Natural language processing
- Search engine optimization
- Algorithmic bias
References
- ^ “What is Generative Engine Optimization (GEO)?” – Conductor, Jun 18, 2025. https://www.conductor.com/blog/generative-engine-optimization/
- ^ Pol, Tushar. “Generative Engine Optimization: The New Era of Search.” Semrush Blog, Jun 6, 2025. https://www.semrush.com/blog/generative-engine-optimization/
- ^ “GEO Fundamentals.” OnMarketing, July 2, 2025.https://www.onmarketing.ai/geo-fundamentals/
- ^ Binder, Adam. “Generative Engine Optimization (GEO): The Future Of Search Is Here.” Forbes, Jan 2, 2025. https://www.forbes.com/sites/adambinder/2025/01/02/generative-engine-optimization-geo-the-future-of-search/