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As the next-generation paradigm for content creation, AI-Generated Content(AIGC), i.e., generating content automatically by Generative AI (GAI) based onuser prompts, has gained great attention and success recently. With theever-increasing power of GAI, especially the emergence of Pretrained FoundationModels (PFMs) that contain billions of parameters and prompt engineeringmethods (i.e., finding the best prompts for the given task), the applicationrange of AIGC is rapidly expanding, covering various forms of information forhuman, systems, and networks, such as network designs, channel coding, andoptimization solutions. In this article, we present the concept of mobile-edgeAI-Generated Everything (AIGX). Specifically, we first review the buildingblocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGXapplications. Then, we present a unified mobile-edge AIGX framework, whichemploys edge devices to provide PFM-empowered AIGX services and optimizes suchservices via prompt engineering. More importantly, we demonstrate thatsuboptimal prompts lead to poor generation quality, which adversely affectsuser satisfaction, edge network performance, and resource utilization.Accordingly, we conduct a case study, showcasing how to train an effectiveprompt optimizer using ChatGPT and investigating how much improvement ispossible with prompt engineering in terms of user experience, quality ofgeneration, and network performance.

Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study