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Large Language Models (LLMs) are revolutionizing the field of computingeducation with their powerful code-generating capabilities. Traditionalpedagogical practices have focused on code writing tasks, but there is now ashift in importance towards code reading, comprehension and evaluation ofLLM-generated code. Alongside this shift, an important new skill is emerging --the ability to solve programming tasks by constructing good prompts forcode-generating models. In this work we introduce a new type of programmingexercise to hone this nascent skill: 'Prompt Problems'. Prompt Problems aredesigned to help students learn how to write effective prompts for AI codegenerators. A student solves a Prompt Problem by crafting a natural languageprompt which, when provided as input to an LLM, outputs code that successfullysolves a specified programming task. We also present a new web-based toolcalled Promptly which hosts a repository of Prompt Problems and supports theautomated evaluation of prompt-generated code. We deploy Promptly for the firsttime in one CS1 and one CS2 course and describe our experiences, which includestudent perceptions of this new type of activity and their interactions withthe tool. We find that students are enthusiastic about Prompt Problems, andappreciate how the problems engage their computational thinking skills andexpose them to new programming constructs. We discuss ideas for the futuredevelopment of new variations of Prompt Problems, and the need to carefullystudy their integration into classroom practice.

Prompt Problems: A New Programming Exercise for the Generative AI Era