As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.

翻译：作为下一代内容创作范式，AI生成内容（AIGC）即基于用户提示由生成式AI自动生成内容的技术，近期已获得广泛关注与成功。随着生成式AI能力的持续提升，特别是包含数十亿参数的预训练基础模型的出现以及提示工程方法（即为给定任务寻找最优提示）的演进，AIGC的应用范围正迅速扩展，覆盖人类、系统及网络所需的各种信息形式，例如网络设计、信道编码与优化方案。本文提出移动边缘AI生成一切（AIGX）的概念。具体而言，我们首先梳理AIGX的构建模块、从AIGC到AIGX的演进历程，以及AIGX的实际应用场景；接着提出统一的移动边缘AIGX框架，该框架利用边缘设备提供基于预训练基础模型的AIGX服务，并通过提示工程优化此类服务。更重要的是，我们证明次优提示会导致生成质量不佳，进而对用户满意度、边缘网络性能及资源利用率产生负面影响。为此，我们开展案例研究，展示如何利用ChatGPT训练有效的提示优化器，并探究提示工程在用户体验、生成质量及网络性能方面所能带来的提升程度。