Imagine chatting with an AI about designing a spaceship. You discuss the mission, the payload, the orbit, the solar panels—each detail adding to a complex, evolving design. But what if the AI has a short memory, forgetting earlier parts of your conversation as you progress? That's the problem with many large language models (LLMs) today. They have limited "context windows," meaning they can only remember a certain number of words or "tokens" at a time. This can make it difficult to discuss intricate topics, like engineering a spacecraft, which involve holding lots of interrelated information simultaneously. New research explores a clever technique to overcome this token limitation, borrowing a tool from the engineering world itself: the Design Structure Matrix (DSM). The DSM helps visualize complex projects by mapping out the dependencies between different components. In the spaceship example, the DSM would show how the design of the propulsion system influences the thermal control system, which in turn affects the electrical system, and so on. By analyzing these connections, researchers found they could cluster related design elements together and sequence the conversation in a way that minimizes the AI's token load. Instead of discussing the entire spaceship at once, you could focus on tightly linked elements like the orbit, the telemetry system, and the ground station in one cluster. Then, shift to the propulsion, power, and thermal systems in another, preserving earlier discussions while staying within the LLM's memory capacity. This approach dramatically reduces the number of tokens the AI needs to process at once, allowing for longer, more detailed conversations. It's like organizing a huge library into themed sections, making it easier to find the information you need without getting overwhelmed. While this research specifically tackles spacecraft design, it has broader implications for any conversation that involves multiple related aspects. Imagine applying it to crafting complex legal arguments, writing intricate narratives, or even just remembering all the details of your next big project. By bridging the gap between engineering design principles and AI language models, the DSM offers a way to unlock more in-depth, nuanced conversations and tame the token tsunamis that limit current AI interactions.