Generative AI agile assistant

Darrell L. Young, Perry Boyette, James Moreland, Jason Teske
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Abstract

Large Language Models (LLMs) provide new capabilities to rapidly reform, regroup; and reskill for new missions, opportunities, and respond to an ever-changing operational landscape. Agile contracts can enable larger flow of value in new development contexts. These methods of engagement and partnership enable the establishment of high performing teams through the forming, storming, norming, and performing stages that then inform the best liberating structures that exceed traditional rigid hierarchical models or even established mission engineering methods. Use of Generative AI based on LLMs coupled with modern agile model-based engineering in design allows for automated requirements decomposition trained in the lingua franca of the development team and translation to the dialects of other domain disciplines with the business acumen afforded by proven approaches in industry. Cutting-edge AI automations to track and adapt knowledge, skills, and abilities across ever changing jobs and roles will be illustrated using prevailing architecture frameworks, model-based system engineering, simulation, and decision-making assisted approaches to emergent objectives.
生成式人工智能敏捷助手
大型语言模型(LLM)提供了新的能力,可针对新任务、新机遇进行快速改革、重组和再培训,并应对不断变化的业务环境。敏捷合同可以在新的发展环境中实现更大的价值流。这些参与和合作方法能够通过组建、风暴、规范和执行阶段建立高绩效团队,然后为最佳解放结构提供信息,这些结构超越了传统的僵化等级模式,甚至超越了既定的任务工程方法。在设计中使用基于 LLMs 的生成式人工智能与基于模型的现代敏捷工程相结合,就能以开发团队的通用语言进行自动需求分解,并将其翻译成其他领域学科的方言,同时还能利用行业中成熟方法所提供的商业敏锐度。我们将利用流行的架构框架、基于模型的系统工程、仿真和决策辅助方法来说明在不断变化的工作和角色中跟踪和调整知识、技能和能力的尖端人工智能自动化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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