This study presents the integration of Artificial Intelligence Operations (AIOps) at the Institute of High Energy Physics (IHEP) Computing Center to address the challenges of managing a complex IT infrastructure and providing efficient user support. We propose a question-answering (QA) system utilizing advancements in large language models and Natural Language Processing (NLP) to facilitate rapid problem resolution and document queries, aiming to reduce the operational staff's workload. The paper highlights the limitations of applying large language models in specialized domains due to insufficient exposure to domain-specific texts. It discusses using targeted fine-tuning and Retrieval-Augmented Generation (RAG) technology to enhance model performance. By compiling Helpdesk QA datasets for fine-tuning the Xiwu model, specializing in high-energy physics, and developing an RAG framework to integrate external knowledge, we significantly improve operational efficiency and support within the IHEP's computing environment.