用GPT让项目管理者用自然语言查Jira数据，省去复杂操作。
Using GPT to build a Project Management assistant for Jira environments
- 基于GPT构建Jira插件，支持自然语言查询项目信息。
- 不同提示词下，GPT在任务完成率上差异明显，最优提示准确率达87%。
- 适合需要快速获取项目数据的项目经理，无需编程基础。
在项目管理领域，数据量庞大给项目经理带来持续挑战。从启动到完成项目，需处理时间线、预算和任务依赖等多种信息流。为高效应对这一数据驱动环境，项目经理需依赖高效且复杂的工具，以实现沟通优化、资源分配与实时决策。然而，许多工具学习成本高，且需使用复杂编程语言才能获取所需数据。本文提出JiraGPT Next，一款基于GPT大语言模型的Jira插件，通过自然语言接口简化数据检索流程。该系统设计了多种提示策略，并评估了不同提示对GPT任务完成率的影响。实验表明，在最佳提示配置下，GPT能实现87%的任务准确率，显著提升数据获取效率。
原文摘要 · Abstract (English)
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.
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