The extensive documentation required during the early phases of space mission design, which must be reviewed and analyzed by systems engineers, often slows down the design process. Given the time-intensive nature of this stage, integrating a Virtual Assistant (VA) can significantly enhance the efficiency of information retrieval. Recent advancements in Natural Language Processing (NLP), particularly the development of Large Language Models (LLMs), have created opportunities for non-invasive question-answering VAs. However, the limited technical knowledge of LLMs in the space domain and their lack of up-to-date information pose significant challenges for real-world applications. Retrieval-Augmented Generation (RAG) offers a solution by incorporating an external knowledge source, from which a retriever selects the most relevant information based on the user’s query.