Recent advancements in Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains. While they exhibit strong zero-shot performance on various tasks, LLMs' effectiveness in music-related applications remains limited due to the relatively small proportion of music-specific knowledge in their training data. To address this limitation, we propose MusT-RAG, a comprehensive framework based on Retrieval Augmented Generation (RAG) to adapt general-purpose LLMs for text-only music question answering (MQA) tasks. RAG is a technique that provides external knowledge to LResearch goal: How does the alignment of multimodal embeddings (e.g., text and audio) in MUST-RAG affect the consistency and robustness of generated answers when evaluated on adversarial or ambiguous music-related questions?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 9.5/10.