Generative artificial intelligence (GAI) represents a prominent research focus in medicine, with medical education being a key application area. GAI demonstrates potential to enhance residency training efficacy through personalized instruction, automated assessment item generation, question bank updating, and intelligent scoring systems. However, current limitations exist regarding output accuracy and content consistency. To address these constraints, strategic measures are required: continuous GAI model refinement, development of standardized usage guidelines, enhanced data quality control, and implementation of human verification protocols for generated content. Concurrently, residents should proactively acquire GAI utilization skills to strengthen the practical application of theoretical knowledge. With these advancements, GAI is anticipated to evolve into a valuable asset for improving the efficiency and quality of residency training programs.