This paper presents a golf swing coaching system that evaluates movement quality and delivers real-time corrective feedback. The system was implemented entirely on an edge AI platform, eliminating reliance on cloud-based infrastructure to ensure low latency, improved data privacy, and reliable operation in offline environments. A structured computer vision pipeline is used where human pose data is extracted from live video using landmark detection, normalized to ensure consistency across users with different body proportions, and transformed into joint-angle representations for analysis. Movement quality is evaluated through comparison with multiple reference poses, enabling robust handling of natural variation in user performance. A finite-state model is used to segment motion into distinct golf swing phases, allowing context-aware feedback generation. By combining efficient geometric computations with lightweight models, the system demonstrates the practicality of a standalone Edge based solutions for real-time motion analysis and coaching applications.