As the dependence on computer systems expands across various domains, focusing on personal, industrial, and large-scale applications, there arises a compelling need to enhance their reliability to sustain business operations seamlessly and ensure optimal user satisfaction. Achieving enhanced reliability depends on predicting and preemptively addressing these failures presents a formidable challenge. However, the challenge of predicting failures and mitigate them in timely manner is a complex problem. Thus, it becomes important to adopt effective strategies for managing failures and minimizing their impact. System logs generated by these devices serve as valuable repositories of histori-cal trends and past failures. The use of machine learning techniques for failure prediction has become commonplace, enabling the extraction of insights from past data to anticipate future behavior patterns. Recently, large language models have demonstrated remarkable capabilities in tasks including summarization, reasoning, and event prediction. Therefore, in this paper, we endeavor to investigate the potential of large language models in predicting system failures, leveraging insights learned from past failure behavior to inform reasoning and decision-making processes effectively. Our approach involves leveraging data from the Intel® Computing Improvement Program (ICIP) system crash logs to identify significant events and develop CrashEventLLM. This model, built upon a large language model framework, serves as our foundation for crash event prediction. Specifically, our model utilizes historical data to forecast future crash events, informed by expert annotations. Additionally, it goes beyond mere prediction, offering insights into potential causes for each crash event. This work provides the preliminary insights into prompt-based large language models for the log-based event prediction task.