Prompt Engineering Language in The Agile Product Backlog Refinement Process

Abstract

Contemporary advanced business services and products increasingly incorporate artificial intelligence components, such as chatbots and generative AI, across various domains. These are delivered through dedicated, complex, innovative software programs and projects initiated by virtually all industry sectors. Complex project management include challenges such as: the complexity of product backlogs with a number of requirements, highly dynamic changes in customer expectations impacting product backlog quality and requirements engineering, labour shortages, advanced tool adoption and automation, predictability of deliveries, insufficient transparency of processes applied to product backlog management, communication barriers between business and project teams. The primary objective of this paper is to address a key research gap related to the insufficient quality of product backlog management in complex agile software project environments. The paper addresses the research question regarding the potential application of chatbots and generative AI as a methodology to conduct reliable agile product backlog evaluation and subsequently enhance refinement processes through dedicated Prompt Engineering Language (PEL). This paper emphasizes the importance of a structured description of product backlog items and its impact on the overall quality of agile product backlog and delivered software products. Following the literature review, the author's empirical research presents a detailed analysis of the research gap and focuses on applying chatbot and generative AI solutions to evaluate agile product backlog items and to improve related agile refinement processes. Research results demonstrate that agile product backlog refinement processes can be supported by chatbots and generative AI utilizing dedicated Prompt Engineering Language (PEL). These tools are not designed to create business value directly but rather enhance the efficiency and automation of product backlog management processes to respond rapidly to stakeholder expectations within agile environments, ultimately achieving superior project outcomes. Nevertheless, numerous challenges must be addressed, particularly related to AI governance and compliance with numerous policies including data privacy, intellectual property, legal, security and internal ones.