Background and Context: Problem decomposition is a fundamen- tal computational thinking skill that novice programmers struggle to develop. Understanding and improving decomposition skills re- mains challenging for educators. Objective: We investigate how students use a “natural language functions” (NLFs) tool that generates callable functions from nat- ural language prompts, examining their decomposition behaviors compared to students without access to this generative AI tool. Method: We conducted a mixed-methods study combining think- aloud protocols, automated metrics collection, and qualitative anal- ysis. In a quasi-experiment, an experimental group (n=6) used NLFs to solve two programming tasks, while a control group (n=5) solved the same tasks without NLFs. Findings: Students with NLFs access exhibited significantly more decomposition behavior, creating approximately three times as many functions as the control group. We observed a shift from verbal articulation to written expression through the prompt in- terface, with experimental students spending less time verbalizing intent and more time crafting prompts. This suggests prompting may serve as a proxy for traditional programming behaviors. Implications: Students who articulate functionality in natural lan- guage demonstrate enhanced decomposition behaviors. Tools like NLFs can serve as valuable pedagogical scaffolds that encourage structured thinking through natural language articulation. By re- quiring students to explicitly describe discrete functions before im- plementation, such tools may make metacognitive processes more visible and teach decomposition skills that are otherwise difficult to convey, potentially improving learning outcomes in introductory programming courses.