In a radiation oncology clinic, machine downtime can be a serious burden to the entire department. This study investigates using increasingly popular generative AI techniques to assist medical physicists in troubleshooting Linear Accelerator (LINAC) issues. Google's NotebookLM, supplemented with background information on LINAC issues/solutions was used as a Machine Troubleshooting Assistant for this purpose. Two board-certified Medical Physicists evaluated the LLM's responses based on hallucination, relevancy, correctness, and completeness. Results indicated that responses improved with increasing source data context and more specific prompt construction. Keeping risk-mitigation and the inherent limitations of AI in mind, this work offers a viable, low-risk method to improve efficiency in radiation oncology. This work uses a "Machine Troubleshooting Assistance" application to provide an adaptable example of how radiation oncology clinics can begin using generative AI to enhance clinical efficiency.