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High-performance computing is a driving force behind scientific innovation and discovery. However, as the number of users and the complexity of high-performance computing systems grow, so does the volume and variability of technical issues handled by sup- port teams. The evolving nature of these issues presents a need for automated tools that can extract clear, accurate, and relevant fre- quently asked questions directly from support tickets. This need was addressed by developing a novel pipeline that incorporates seman- tic clustering, representation learning, and large language models. While prior research laid strong foundations across classification, clustering and large language model-based questions & answers, our work augments these efforts by integrating semantic clustering, domain-specific summarization, and multi-stage generation into a scalable pipeline for autonomous technical support. To prioritize high-impact issues, the pipeline began by filtering tickets based on anomaly frequency and recency. It then leveraged an instruction- tuned large language model to clean and summarize each ticket into a structured issue-resolution pair. Next, unsupervised semantic clus- tering was performed to identify subclusters of semantically similar tickets within broader topic clusters. A large language model-based generation module was then applied to create frequently asked questions representing the most dominant issues. A structured evaluation by subject matter experts indicated that our approach transformed technical support tickets into understandable, factu- ally sound, and pertinent frequently asked questions. The ability to extract fine-grained insights from raw ticket data enhances the scalability, efficiency, and responsiveness of technical support work- flows in high-performance computing environments, ultimately enabling faster troubleshooting and more accessible pathways to scientific discovery.