Large Language Models (LLMs) have the potential to revolutionize automated traceability by overcoming the challenges faced by previous methods and introducing new possibilities. However, the optimal utilization of LLMs for automated traceability remains unclear. This paper explores the process of prompt engineering to extract link predictions from an LLM. We provide detailed insights into our approach for constructing effective prompts, offering our lessons learned. Additionally, we propose multiple strategies for leveraging LLMs to generate traceability links, improving upon previous zero-shot methods on the ranking of candidate links after prompt refinement. The primary objective of this paper is to inspire and assist future researchers and engineers by highlighting the process of constructing traceability prompts to effectively harness LLMs for advancing automatic traceability.

翻译：大型语言模型（LLM）具有通过克服先前方法所面临的挑战并引入新可能性来革新自动化可追溯性的潜力。然而，如何最优地利用LLM实现自动化可追溯性仍不明确。本文探索了通过提示工程从LLM中提取链接预测的过程。我们详细阐述了构建有效提示的方法，并分享了经验教训。此外，我们提出了多种利用LLM生成可追溯性链接的策略，在提示优化后改进了先前零样本方法在候选链接排序方面的表现。本文的主要目标是通过强调构建可追溯性提示的过程，以有效利用LLM推动自动化可追溯性发展，从而启发并协助未来的研究人员与工程师。