Product managers are now tasked with understanding how AI interprets their products, not just developing features. When an AI assistant summarizes a product, its response can miss key aspects, focus on outdated information, or even favor competitors. This discrepancy highlights the need for a systematic approach to manage AI perception, which has become a crucial factor in how potential customers evaluate products before engaging with sales teams. The Product Perception Loop is a method for product managers to assess and improve AI's understanding of their offerings. It involves creating a "Golden Set" of key questions that reflect buyer interests, using multiple AI tools to gather responses, and evaluating those responses based on attribution, accuracy, and differentiation. By identifying gaps in AI's responses—like missing information or incorrect comparisons—product managers can prioritize targeted fixes, whether they relate to product context, documentation, or marketing strategies. Implementing this loop is not a one-off task. It’s about establishing a learning system that grows over time. Initial runs will provide a baseline understanding of AI's perception, revealing strengths and weaknesses in the product's visibility and positioning. Regular iterations refine this process, making AI perception a valuable signal alongside traditional metrics like user feedback and feature adoption. By continuously adjusting and testing, product managers can ensure that AI aligns more closely with their product’s intended messaging and value.