  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), can generate accurate and
personalized responses, rapidly replacing traditional search engines like
Google and Bing. Generative Engines typically satisfy queries by synthesizing
information from multiple sources and summarizing them using LLMs. While this
shift significantly improves $\textit{user}$ utility and $\textit{generative
search engine}$ traffic, it poses 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
$\textit{when}$ and $\textit{how}$ their content is displayed. With generative
engines here to stay, we must ensure the creator economy is not disadvantaged.
To address this, we introduce Generative Engine Optimization (GEO), the first
novel paradigm to aid content creators in improving their content visibility in
generative engine responses through a flexible black-box optimization framework
for optimizing and defining visibility metrics. We facilitate systematic
evaluation by introducing GEO-bench, a large-scale benchmark of diverse user
queries across multiple domains, along with relevant web sources to answer
these queries. Through rigorous evaluation, we demonstrate 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 optimization methods. Our work opens a new frontier in
information discovery systems, with profound implications for both developers
of generative engines and content creators.
