The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. This emerging technology, which we formalize under the unified framework of Generative Engines (GEs), has the potential to generate accurate and personalized responses, and is rapidly replacing traditional search engines like Google and Bing. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them with the help of LLMs. While this shift significantly improves \textit{user} utility and \textit{generative search engine} traffic, it results in a huge challenge for the third stakeholder -- website and content creators. Given the black-box and fast-moving nature of Generative Engines, content creators have little to no control over when and how their content is displayed. With generative engines here to stay, the right tools should be provided to ensure that creator economy is not severely disadvantaged. To address this, we introduce Generative Engine Optimization (GEO), a novel paradigm to aid content creators in improving the visibility of their content in Generative Engine responses through a black-box optimization framework for optimizing and defining visibility metrics. We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries. Through rigorous evaluation, we show that GEO can boost visibility by up to 40\% in generative engine responses. Moreover, we show the efficacy of these strategies varies across domains, underscoring the need for domain-specific methods. Our work opens a new frontier in the field of information discovery systems, with profound implications for generative engines and content creators.

大型语言模型的出现引入了一种新的搜索引擎范式，使用生成模型来收集和总结信息以回答用户查询。我们正式将这种新兴技术统一归类为生成引擎，并认为它具有生成准确和个性化的回答的潜力，正在快速取代谷歌和必应等传统搜索引擎。为了解决内容创作者面临的挑战，我们引入了生成引擎优化（GEO），这是一种通过黑盒优化框架来优化和定义可见性指标，以帮助内容创作者提高其内容在生成引擎回应中的可见性，且经过严格评估，我们展示了GEO在生成引擎回应中可提高可见性高达40％的效果，同时也强调了对特定领域的需求。我们的工作在信息发现系统领域开辟了新的前沿，并对生成引擎和内容创作者产生了深远的影响。

GEO: 生成引擎优化