Human-Centered AI (HCAI) challenges conventional user-centered design by emphasizing strategies that account for the evolving, adaptive, and probabilistic nature of AI systems. As AI models continuously change, user experiences must accommodate shifting system behaviors, varying contexts, and inherent uncertainties. Designing for such systems requires collaboration across multiple disciplines and interfaces that foster transparency, explainability, and human oversight, enabling users to understand, trust, and, when necessary, intervene in system decisions. A central objective of HCAI experiences is establishing calibrated trust between users and AI, which guides choices about when AI should automate tasks, support human judgment, or share control with the user. Considering the probabilistic outputs of AI, incorporating mechanisms for explicit and implicit user feedback is essential to continuously refine system performance and align behavior with user expectations. By integrating these principles, designers can create AI-driven experiences that are reliable, ethically informed, and human-centered; prioritizing explainability, user agency, trust, and iterative feedback along with user experience as core pillars of HCAI.