GEO represents a new paradigm that will help content creators optimize their content for the next generation of search engines, which are enhanced by large language models. With the emergence of large language models, a new type of search engine that uses generative models to collect and summarize information to answer user queries has emerged. These “generative engines” can generate accurate and personalized responses and are rapidly replacing traditional search engines, enhancing user utility. However, generative engines also present many challenges for content creators: **Ranking and Visibility:** In traditional search engines, evaluating website performance was straightforward; websites were listed in order of ranking, displaying the original content. However, generative engines can generate rich and structured responses, often embedding citations within a block. This makes the concepts of ranking and visibility very subtle and multifaceted. **Website Optimization:** Unlike search engines where extensive research exists on improving website visibility, the methods for optimizing visibility in generative engine responses remain unclear. **Black Box Nature:** Due to the black-box nature of generative engines, content creators have little control over when and how their content is displayed, making content optimization more difficult. To address these challenges, we introduce GEO (Generative Engine Optimization), a new paradigm aimed at helping content creators improve the visibility of their content. We contribute a suite of impression metrics, evaluation benchmarks, and optimization strategies, empowering creators to improve content visibility targeting deployed commercial generative engines.