From Domain Documents to Requirements: Retrieval-Augmented Generation in the Space Industry

TL;DR

This paper explores using Retrieval-Augmented Generation models to semi-automate requirements generation from space mission documents, aiming to reduce manual effort and improve compliance in the space industry.

Contribution

It introduces a modular AI-driven approach combining document classification, retrieval, and language models for requirements engineering in space missions.

Findings

Reduces manual effort in requirements extraction

Enhances coverage of relevant requirements

Supports lightweight compliance alignment

Abstract

Requirements engineering (RE) in the space industry is inherently complex, demanding high precision, alignment with rigorous standards, and adaptability to mission-specific constraints. Smaller space organisations and new entrants often struggle to derive actionable requirements from extensive, unstructured documents such as mission briefs, interface specifications, and regulatory standards. In this innovation opportunity paper, we explore the potential of Retrieval-Augmented Generation (RAG) models to support and (semi-)automate requirements generation in the space domain. We present a modular, AI-driven approach that preprocesses raw space mission documents, classifies them into semantically meaningful categories, retrieves contextually relevant content from domain standards, and synthesises draft requirements using large language models (LLMs). We apply the approach to a real-world mission document from the space domain to demonstrate feasibility and assess early outcomes in collaboration with our industry partner, Starbound Space Solutions. Our preliminary results indicate that the approach can reduce manual effort, improve coverage of relevant requirements, and support lightweight compliance alignment. We outline a roadmap toward broader integration of AI in RE workflows, intending to lower barriers for smaller organisations to participate in large-scale, safety-critical missions.