Large language models (LLMs) are increasingly used as coding partners, yet their role in accelerating scientific discovery remains underexplored. This paper presents a case study of using ChatGPT for rapid prototyping in ESA's ELOPE (Event-based Lunar OPtical flow Egomotion estimation) competition. The competition required participants to process event camera data to estimate lunar lander trajectories. Despite joining late, we achieved second place with a score of 0.01282, highlighting the potential of human-AI collaboration in competitive scientific settings. ChatGPT contributed not only executable code but also algorithmic reasoning, data handling routines, and methodological suggestions, such as using fixed number of events instead of fixed time spans for windowing. At the same time, we observed limitations: the model often introduced unnecessary structural changes, gets confused by intermediate discussions about alternative ideas, occasionally produced critical errors and forgets important aspects in longer scientific discussions. By analyzing these strengths and shortcomings, we show how conversational AI can both accelerate development and support conceptual insight in scientific research. We argue that structured integration of LLMs into the scientific workflow can enhance rapid prototyping by proposing best practices for AI-assisted scientific work.

翻译：大型语言模型（LLM）正日益被用作编程伙伴，但它们在加速科学发现方面的作用仍未得到充分探索。本文通过欧空局ELOPE（基于事件的月球光流自运动估计）竞赛中利用ChatGPT进行快速原型设计的案例研究，探讨了这一议题。该竞赛要求参赛者处理事件相机数据以估计月球着陆器的运动轨迹。尽管我们较晚加入竞赛，仍以0.01282的得分获得第二名，凸显了人机协作在竞争性科学场景中的潜力。ChatGPT不仅贡献了可执行代码，还提供了算法推理、数据处理例程和方法论建议（例如使用固定事件数量而非固定时间跨度进行窗口划分）。同时，我们也观察到其局限性：该模型经常引入不必要的结构变更，在关于替代方案的中间讨论中容易产生混淆，偶尔会产生关键错误，并在较长的科学讨论中遗忘重要方面。通过分析这些优势与不足，我们展示了对话式人工智能如何既能加速科研开发，又能支持科学概念洞察。我们认为，通过制定人工智能辅助科研的最佳实践，将LLM结构化地整合到科学工作流程中，能够有效增强快速原型设计能力。