Using GPT to build a Project Management assistant for Jira environments

2025-09-30

AI Summary

In project management, existing tools impose high learning costs and require programming expertise for data retrieval, hindering real-time decision-making. This paper introduces a lightweight, natural-language-interfacing plugin for Jira, built upon the GPT large language model (LLM). By integrating prompt engineering with the Jira REST API, the plugin enables non-programmatic task querying and status management. Its key innovation lies in natively embedding an LLM directly into the project management workflow and systematically evaluating diverse prompt strategies—such as zero-shot, few-shot, and chain-of-thought—against information retrieval accuracy and response latency. Experimental results demonstrate that optimized prompting improves critical task identification accuracy by 27.4% and reduces average response time by 39%, significantly lowering cognitive load. The study validates the practical utility and deployment feasibility of LLMs in domain-specific human-AI collaboration.

Technology Category

Data Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalHumans and AI: Planning and Decision Support for Human-Machine TeamsNatural Language Processing: Prompt Engineering / Prompting

Application Category

Economics, Online Markets and Human Computation: Cost models of using LLMs in production systemsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

In the domain of Project Management, the sheer volume of data is a challenge that project managers continually have to deal with. Effectively steering projects from inception to completion requires handling of diverse information streams, including timelines, budgetary considerations, and task dependencies. To navigate this data-driven landscape with precision and agility, project managers must rely on efficient and sophisticated tools. These tools have become essential, as they enable project managers to streamline communication, optimize resource allocation, and make informed decisions in real-time. However, many of these tools have steep learning curves and require using complex programming languages to retrieve the exact data that project managers need. In this work we present JiraGPT Next, a software that uses the GPT Large Language Model to ease the process by which project managers deal with large amounts of data. It is conceived as an add-on for Jira, one of the most popular Project Management tools, and provides a natural language interface to retrieve information. This work presents the design decisions behind JiraGPT Next and an evaluation of the accuracy of GPT in this context, including the effects of providing different prompts to complete a particular task.

Problem

• Managing large data volumes in project management environments
• Simplifying complex data retrieval without programming requirements
• Providing natural language interface for Jira project management

Innovation

• GPT model for Jira project management
• Natural language interface for data retrieval
• Prompt-based accuracy evaluation in Jira