@techreport {pub6497,
	title = {Conversational AI for Rapid Scientifi c Prototyping:
A Case Study on ESA{\textquoteright}s ELOPE Competition},
	author = {Nils Einecke},
	year = {2026},
	month = {January},
	abstract = {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{\textquoteright}s ELOPE (Event-based Lunar OPtical fl ow 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{\textendash}AI collaboration in competitive scientifi c settings. ChatGPT contributed not only executable code but also algorithmic reasoning, data handling routines, and methodological suggestions, such as using fi xed 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 scientifi c discussions. By analyzing these strengths and shortcomings, we show how conversational AI can both accelerate development and support conceptual insight in scientifi c research. We argue that structured integration of LLMs into the scientifi c workfl ow can enhance rapid prototyping by proposing best practices for AI-assisted scientific work.},
	publisher = {arxiv},
	booktitle = {arxiv}
}