This study explores how multiple levels of GPT-generated programming hints support or disappoint novices. Researchers conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system that provides four levels of hints: general natural language guidance, instrumental hints, worked example hints, and bottom-out hints. The findings show that high-level natural language hints alone can be insufficient or misleading, especially for next-step or syntax-related help requests. Adding lower-level hints, such as code examples with inline comments, can better support students. The study highlights the need to customize help responses based on content, format, and granularity to meet students' diverse needs. It also reveals that worked example hints are particularly effective for next-step and syntax-related requests, while high-level hints can sometimes cause confusion or frustration. The study suggests that future instructional agents should be designed to provide personalized, context-specific help to support students' learning. The LLM Hint Factory is a novel system that provides scalable, high-quality programming hints at various levels, helping students navigate complex programming tasks. The study contributes to the field by emphasizing the importance of tailoring help responses to students' needs and demonstrating the effectiveness of multi-level hints in supporting novices.This study explores how multiple levels of GPT-generated programming hints support or disappoint novices. Researchers conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system that provides four levels of hints: general natural language guidance, instrumental hints, worked example hints, and bottom-out hints. The findings show that high-level natural language hints alone can be insufficient or misleading, especially for next-step or syntax-related help requests. Adding lower-level hints, such as code examples with inline comments, can better support students. The study highlights the need to customize help responses based on content, format, and granularity to meet students' diverse needs. It also reveals that worked example hints are particularly effective for next-step and syntax-related requests, while high-level hints can sometimes cause confusion or frustration. The study suggests that future instructional agents should be designed to provide personalized, context-specific help to support students' learning. The LLM Hint Factory is a novel system that provides scalable, high-quality programming hints at various levels, helping students navigate complex programming tasks. The study contributes to the field by emphasizing the importance of tailoring help responses to students' needs and demonstrating the effectiveness of multi-level hints in supporting novices.