Automated log analysis plays a crucial role in software maintenance as it allows for efficient identification and resolution of issues. However, traditional methods employed in log analysis heavily rely on extensive historical data for training purposes and lack rationales for its predictions. The performance of these traditional methods significantly deteriorates when in-domain logs for training are limited and unseen log data are the majority, particularly in rapidly changing online environments. Additionally, the lack of rationales hampers the interpretability of analysis results and impacts analysts’ subsequent decision-making processes. To address these challenges, we proposes LogPrompt, an novel approach that leverages large language models (LLMs) and advanced prompting techniques to achieve performance improvements in zero-shot scenarios (i.e., no in-domain training). Moreover, LogPrompt has garnered positive evaluations from experienced practitioners in its log interpretation ability. We release the code of LogPrompt.