Wi-Fi technology has emerged as a promising solution for contact-free sensing owing to the pervasiveness of Wi-Fi signals in indoor environments. However, Wi-Fi sensing faces several fundamental issues, including limited sensing range and unstable orientation-dependent sensing performance, hindering the widespread adoption of Wi-Fi sensing in real-life scenarios. In this paper, we propose RISensing, a novel system that leverages Reconfigurable Intelligent Surfaces (RIS) to address these two fundamental issues of Wi-Fi sensing and bring Wi-Fi sensing one step closer to real-world adoption. Unlike prior Wi-Fi sensing works which typically rely on a single target reflection signal to capture the target movement, RISensing utilizes two target reflection signals, i.e., the direct target reflection signal and RIS-based target reflection signal, to boost the sensing capability. RISensing characterizes the RIS-based target reflection signal, and constructively combines it with the direct target reflection. We evaluate the sensing performance of RISensing in various environments, including corridor, office and lab. Extensive experiments demonstrate RISensing can improve the sensing range of Wi-Fi from 4 m to 23 m, and effectively mitigate the orientation-dependent issue.