Large Language Models (LLMs) like ChatGPT are generative AI that can answer questions and provide solutions to problems in various domains. To date, little is known about how students interact with LLM tools while problem solving. We recruited novice programmers (N=21) and gave them a brief video-based lesson on the fundamentals. We then asked them to use ChatGPT (1) to learn about one additional construct, namely for loops, and (2) subsequently solve four problems that required specifying the output of a program with a for loop, with access to ChatGPT. Participants verbalized their thoughts as they worked, and these were transcribed and analyzed. We used a qualitative approach to identify how students reasoned and what strategies they employed while solving problems. We found substantially more independent problem solving than expected given prior reports on students’ over-reliance on LLMs.