Large Language Models generate functional code for common scenarios but consistently miss edge cases, revealing fundamental limitations in their reasoning capabilities. This article examines why pattern-matching AI struggles with boundary conditions and explores emerging solutions that combine LLMs with systematic testing frameworks.