Large language models (LLMs) have revolutionized programming by enabling non-technical users to write code, yet questions remain about their understanding of code concepts, particularly nullability. This article explores how LLMs infer nullability through internal representations and offers insights into their reasoning processes when generating code, highlighting both their strengths and limitations in handling nullable types.