Large Language Models can assist in coding but often lead to inefficiencies due to their lack of contextual understanding and tendency to consume excessive resources. By utilizing semantic understanding and vector embeddings, developers can improve the effectiveness of AI coding agents, minimizing time and token waste while enhancing codebase navigation through better function summarization and dependency management.