This study investigates the formation of expert teams that collectively possess a specified skill set. While traditional methods have employed graph search techniques to identify subgraphs that meet skill requirements or neural architectures to map skills to experts, we introduce a novel approach that emphasizes both cohesive team dynamics and comprehensive skill coverage. Our retrieval-augmented generation model is designed to optimize the probability of successful collaboration among team members. Extensive experiments demonstrate that our proposed method significantly outperforms existing state-of-the-art approaches, offering a more effective solution for expert team formation.