Speaker
Description
Large-scale accelerator facilities such as GSI/FAIR rely heavily on distributed expertise and fragmented documentation for daily operations, creating persistent challenges in knowledge retention, troubleshooting speed, and workforce continuity. To address these operational bottlenecks, we present an AI assistant designed to deliver context aware, expert level guidance to shift operators. Grounded in large language models (LLM) run locally on the GSI high performance computing infrastructure, the system integrates retrieval augmented generation (RAG) and structured prompt engineering to synthesize domain knowledge from internal wikis, technical manuals, historical logbooks, and online shift data. In addition to the prof of principle status, we present future development plans including incorporation of more knowledge sources, exploration of different LLM models and low-rank adaption and integration with the control system to access accelerator data in real-time.