🤖 AI Summary
This study addresses the challenges of decision latency and information overload in space operations caused by the vast volume of technical documentation and scientific literature. It presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) for this high-stakes domain, integrating multiple retrieval strategies, embedding models, and large language models to efficiently extract and synthesize actionable knowledge from domain-specific documents. Experimental results demonstrate that the proposed RAG pipeline substantially enhances the accuracy, relevance, and reliability of knowledge retrieval, thereby reducing decision uncertainty. The work delivers a practical and trustworthy intelligent support framework for complex space missions while delineating clear pathways for optimization and defining the boundaries of its applicability.
📝 Abstract
The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Effective management in space operations requires tools capable of efficiently processing vast and heterogeneous information sources. This paper systematically evaluates the performance of Retrieval Augmented Generation (RAG) pipelines, combining Large Language Models (LLMs) with information retrieval techniques for extracting and synthesizing actionable knowledge from domain-specific documents. We compare various retrieval strategies, embedding models, and LLM answers to assess their impact on information accuracy, relevance, and reliability. Our results demonstrate that RAG pipelines can significantly enhance knowledge access, reduce uncertainty, and support decision-making in complex space operations.