Efficient and intuitive programming strategies are essential for enabling robots to adapt to small-batch, high-mix production scenarios. Mixed reality (MR) and programming by demonstration (PbD) have shown great potential to lower the programming barrier and enhance human–robot interaction by leveraging natural human guidance. However, traditional offline programming methods, while capable of generating industrial-grade trajectories, remain time-consuming, costly to debug, and heavily dependent on expert knowledge. Conversely, existing MR-based PbD approaches primarily focus on improving intuitiveness but often suffer from low trajectory quality due to hand jitter and the lack of refinement mechanisms. To address these limitations, this paper introduces a coarse-to-fine human–robot collaborative programming paradigm. In this paradigm, the operator’s role is elevated from a low-level “trajectory drawer” to a high-level “task guider”. By leveraging sparse key points as guidance, the paradigm decouples high-level human task intent from machine-level trajectory planning, enabling their effective integration. The feasibility of the proposed system is validated through two industrial case studies and comparative quantitative experiments against conventional programming methods. The results demonstrate that the coarse-to-fine paradigm significantly improves programming efficiency and usability while reducing operator cognitive load. Crucially, it achieves this without compromising the final output, automatically generating smooth, high-fidelity trajectories from simple user inputs. This work provides an effective pathway toward reconciling programming intuitiveness with final trajectory quality.