Generative AI: strategies and use cases for astronomy

Abstract

Generative AI (GenAI) promises a disruptive impact across multiple research and operational activities, including the domain of astrophysics and the development of astronomical instrumentation. This rapidly evolving tech nology requires a cross-disciplinary, systemic approach to ensure effective integration into astronomical research workflows. Its adoption also requires strict governance to ensure scientific reproducibility while addressing ethical and regulatory concerns. Gen.IA, a dedicated study group within the Italian National Institute of Astrophysics (INAF), proposes a structured strategy to bridge the gap between experimental AI and operational deployment. As a first step, the study group launched an internal survey1 to assess current adoption levels, expectations, and ethical sentiments about AI within the Institute; we present results from this survey. We also outline a strategic roadmap involving: knowledge-sharing frameworks (internal meetings, web resources, newsletters) to disseminate best practices and emerging standards; upskilling for both technical experts and general staff to foster responsible adoption; seminars with external experts to address ethical and regulatory concerns. Additionally, we propose specific use cases for investigation: Retrieval-Augmented Generation (RAG) systems for mining complex technical documentation and driving requirement management, improving traceability and compliance in instrument development and system engineering workflows; Agentic AI for orchestrating automated data analysis, software development, and optimizing research pipelines; AI-assisted literature review to accelerate knowledge discovery in astrophysical domains. This work proposes a strategic framework for using GenAI to enhance astronomical research activities and instrument development, emphasizing the balance between innovation, best practices, and scientific integrity.