  [Context]: Companies are increasingly recognizing the importance of
automating Requirements Engineering (RE) tasks due to their resource-intensive
nature. The advent of GenAI has made these tasks more amenable to automation,
thanks to its ability to understand and interpret context effectively.
[Problem]: However, in the context of GenAI, prompt engineering is a critical
factor for success. Despite this, we currently lack tools and methods to
systematically assess and determine the most effective prompt patterns to
employ for a particular RE task. [Method]: Two tasks related to requirements,
specifically requirement classification and tracing, were automated using the
GPT-3.5 turbo API. The performance evaluation involved assessing various
prompts created using 5 prompt patterns and implemented programmatically to
perform the selected RE tasks, focusing on metrics such as precision, recall,
accuracy, and F-Score. [Results]: This paper evaluates the effectiveness of the
5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks
and offers recommendations on which prompt pattern to use for a specific RE
task. Additionally, it also provides an evaluation framework as a reference for
researchers and practitioners who want to evaluate different prompt patterns
for different RE tasks.
