  Generative AI models, specifically large language models (LLMs), have made
strides towards the long-standing goal of text-to-code generation. This
progress has invited numerous studies of user interaction. However, less is
known about the struggles and strategies of non-experts, for whom each step of
the text-to-code problem presents challenges: describing their intent in
natural language, evaluating the correctness of generated code, and editing
prompts when the generated code is incorrect. This paper presents a large-scale
controlled study of how 120 beginning coders across three academic institutions
approach writing and editing prompts. A novel experimental design allows us to
target specific steps in the text-to-code process and reveals that beginners
struggle with writing and editing prompts, even for problems at their skill
level and when correctness is automatically determined. Our mixed-methods
evaluation provides insight into student processes and perceptions with key
implications for non-expert Code LLM use within and outside of education.
