Cyber-Physical Systems (CPS) increasingly leverage Reinforcement Learning (RL) to adapt dynamically to chang- ing environments and optimize performance over time. While RL enhances efficiency and safety by enabling autonomous adjustments to unexpected conditions and hazard avoidance, it also introduces significant risks, as learned behaviors may lead to unpredictable or unsafe actions in real-world deploy- ment. Therefore, integrating risk management into RL system design is essential. In this paper, we propose the StructuraRL Design Framework, a question-driven approach that translates high-level safety guidelines into RL-specific considerations. This framework helps RL practitioners address key risks early in development, informing new or existing system requirements while ensuring traceability to risk management objectives. To evaluate its effectiveness, we conducted a study across two use cases, engaging six RL experts in developing system requirements with and without the framework. Our findings suggest that the framework promotes critical thinking and helps practitioners identify additional risk factors, ultimately supporting safer RL deployment.