Industrial communication software (such as GNU Radio) serves as a critical bridge connecting wireless communication theory with engineering practice, occupying a core position in the modern radio industry. However, such software is typically highly complex and has a vast API, leaving beginners with a significant cognitive gap between “domain theory” and “software implementation." Existing teaching support is often limited by the static nature of documentation and the lack of specific domain knowledge in general programming tools, making it difficult to provide effective real-time guidance. To address this challenge in industrial software education, this paper proposes an intelligent teaching tool enhanced by Large Language Models—GRIA. GRIA innovatively leverages Retrieval-Augmented Generation (RAG) to map beginners’ natural language requests to precise domain knowledge and software operation sequences, aiming to address the specific problems of API misuse and configuration difficulties in industrial software teaching. Through controlled experiments in real teaching scenarios, results show that GRIA can reduce the total time required for teaching by over 65%, decrease instructors’ repetition rate on low-level debugging tasks by 69.4%, and significantly reduce learning time by approx. 70%. The successful practice of GRIA provides a new paradigm for the application of Large Language Models in complex industrial software engineering education.