This paper explores the integration of Large Language Models (LLMs) into music source separation by leveraging prompt engineering and Retrieval-Augmented Generation (RAG) techniques. Recent advancements in artificial intelligence have reshaped music technology, yet conventional source separation methods remain limited by their reliance on resource-intensive retraining and complex signal processing. These traditional approaches often struggle with diverse musical genres and intricate audio environments, hindering innovation in music production, restoration, and analysis. To address these challenges, we propose an innovative framework that utilizes prompt engineering to guide LLMs in deconstructing composite audio signals into individual musical components without additional model retraining. RAG is employed to dynamically incorporate external musical data—such as scores, lyrics, and isolated instrument tracks—enhancing the model’s contextual understanding and separation accuracy. Our experimental, mixed-method study evaluates this framework through quantitative performance metrics, including separation fidelity and computational efficiency, as well as qualitative feedback from audio professionals regarding usability and practical application. Preliminary results indicate that the LLM-based approach outperforms traditional methods by offering improved adaptability and efficiency, thereby potentially redefining standards in audio processing. The research contributes to the academic discourse by extending the capabilities of LLMs into non-textual domains and providing a foundation for future interdisciplinary studies. Moreover, the proposed method presents practical benefits for music producers, sound engineers, and educators, promising to streamline workflows and foster greater creative expression in modern music technology.