The Context Window Architecture (CWA) is proposed as a disciplined framework for structuring prompts in large language models (LLMs), addressing their limitations such as statelessness and cognitive fallibility. By organizing context into 11 distinct layers, CWA aims to enhance prompt engineering, leading to more reliable and maintainable AI interactions. Feedback and collaboration on this concept are encouraged to refine its implementation in real-world scenarios.