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This study embarked on a comprehensive exploration of user preferencesbetween Search Engines and Large Language Models (LLMs) in the context ofvarious information retrieval scenarios. Conducted with a sample size of 100internet users (N=100) from across the United States, the research delved into20 distinct use cases ranging from factual searches, such as looking upCOVID-19 guidelines, to more subjective tasks, like seeking interpretations ofcomplex concepts in layman's terms. Participants were asked to state theirpreference between using a traditional search engine or an LLM for eachscenario. This approach allowed for a nuanced understanding of how usersperceive and utilize these two predominant digital tools in differing contexts.The use cases were carefully selected to cover a broad spectrum of typicalonline queries, thus ensuring a comprehensive analysis of user preferences. Thefindings reveal intriguing patterns in user choices, highlighting a cleartendency for participants to favor search engines for direct, fact-basedqueries, while LLMs were more often preferred for tasks requiring nuancedunderstanding and language processing. These results offer valuable insightsinto the current state of digital information retrieval and pave the way forfuture innovations in this field. This study not only sheds light on thespecific contexts in which each tool is favored but also hints at the potentialfor developing hybrid models that leverage the strengths of both search enginesand LLMs. The insights gained from this research are pivotal for developers,researchers, and policymakers in understanding the evolving landscape ofdigital information retrieval and user interaction with these technologies.

Large Language Models vs. Search Engines: Evaluating User Preferences Across Varied Information Retrieval Scenarios