Imagine trying to create a perfect map of the entire world, down to every last detail. It's an impossible task—the world is constantly changing, and such a map would be too complex to be useful. AI policy faces a similar challenge. Large Language Models (LLMs) can generate an endless variety of outputs, making it incredibly difficult to predict and control their behavior in every scenario. A new research paper proposes a novel approach to LLM policy design inspired by mapmaking: instead of aiming for complete control, we can create targeted "maps" that prioritize the most crucial areas. This approach, embodied in a tool called Policy Projector, allows AI safety experts to "survey" the landscape of LLM behavior using datasets of inputs and outputs. They can then define custom "regions" or concepts (e.g., "violence," "medical advice") and create rules, or policies, that govern how the LLM should behave within these regions. For example, a policy might specify that if the LLM generates text containing "violence" and "graphic details," it should rewrite the text without the graphic details. This iterative process enables continuous refinement and adaptation. In a study with AI safety experts, Policy Projector helped identify previously unknown policy gaps and design practical solutions. Participants created diverse policies addressing various problematic outputs, demonstrating the tool's flexibility. One expert noted the importance of this visual, grounded approach, especially when discussing complex or abstract policy issues with stakeholders. Another participant highlighted the ability to define custom concepts, which is crucial because different AI features and cultural contexts require tailored guidelines. This mapmaking approach offers a promising way to move beyond reactive bug fixes and towards proactive, iterative design of LLM policies, ensuring greater safety and control over AI behavior.