This study explores how computing students use generative AI tools like ChatGPT for help-seeking in their courses. The research involved surveys and interviews with 47 students and 8 additional students. The findings suggest that while generative AI tools are being rapidly adopted, they have not yet fully replaced traditional help resources. Students' help-seeking preferences vary depending on the task and other factors. The study also found that help-seeking with generative AI is a skill that needs to be developed, with disproportionate benefits for those who are better able to harness the capabilities of LLMs. Students often prefer generative AI tools for tasks like writing code and debugging due to their ability to provide iterative support and reduce social pressures. However, students also value traditional resources like instructors and TAs for their reliability and quality. The study highlights the importance of clear and precise help requests when using generative AI tools, as the quality of assistance received is closely tied to the clarity of the initial request. The research also notes that students who are more experienced with generative AI tools tend to use them more effectively, while less experienced students may be more hesitant due to concerns about accuracy and reliability. Overall, the study suggests that generative AI tools can be a valuable resource for computing students, but their effectiveness depends on how well students can utilize them. The findings have implications for integrating generative AI into computing classrooms and the future of help-seeking in the era of generative AI.This study explores how computing students use generative AI tools like ChatGPT for help-seeking in their courses. The research involved surveys and interviews with 47 students and 8 additional students. The findings suggest that while generative AI tools are being rapidly adopted, they have not yet fully replaced traditional help resources. Students' help-seeking preferences vary depending on the task and other factors. The study also found that help-seeking with generative AI is a skill that needs to be developed, with disproportionate benefits for those who are better able to harness the capabilities of LLMs. Students often prefer generative AI tools for tasks like writing code and debugging due to their ability to provide iterative support and reduce social pressures. However, students also value traditional resources like instructors and TAs for their reliability and quality. The study highlights the importance of clear and precise help requests when using generative AI tools, as the quality of assistance received is closely tied to the clarity of the initial request. The research also notes that students who are more experienced with generative AI tools tend to use them more effectively, while less experienced students may be more hesitant due to concerns about accuracy and reliability. Overall, the study suggests that generative AI tools can be a valuable resource for computing students, but their effectiveness depends on how well students can utilize them. The findings have implications for integrating generative AI into computing classrooms and the future of help-seeking in the era of generative AI.