GEO (generative engine optimisation)
GEO is the practice of increasing how often and how prominently a source is used or cited in answers written by generative AI systems such as AI Overviews, AI Mode, ChatGPT, Gemini and Perplexity.
GEO takes its name from the paper “GEO: Generative Engine Optimization” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, first posted to arXiv in November 2023 and accepted to KDD 2024. The authors describe generative engines as search systems that use generative models to gather and summarise information in answer to a query. They proposed GEO as a framework for improving a source’s visibility in those answers, built a benchmark of queries called GEO-bench and reported that some content changes raised visibility by up to 40%.
Why it matters now: an AI answer often sits above or in place of the list of links, so being cited inside it is a visibility goal of its own.
Be careful with tactics. Results from one benchmark or one engine do not always carry over to others, and Google’s 2026 guide says that for Google Search, GEO is still SEO.
Practical note: test any GEO claim against your own queries on the engines your audience uses, and measure citation frequency over repeated runs.