提出构建稳健自适应生成式AI系统的设计原则与架构模式。
Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
- 基于五大支柱设计生成式AI原生系统，融合软件工程方法。
- 提出GenAI原生单元、有机底座等架构模式提升系统韧性。
- 适合研究者和开发者参考，推动生成式AI系统落地实践。
生成式AI（GenAI）已成为变革性技术，在多个应用领域展现出卓越能力。然而，由于其不可预测性和低效性，当前在构建可靠高效的生成式AI赋能系统方面仍面临诸多挑战。本文倡导范式转变：未来的生成式AI原生系统应将生成式AI的认知能力与传统软件工程原则相结合，以实现稳健、自适应与高效。我们提出了围绕可靠性、卓越性、可演化性、自立性与保障性五大核心支柱的生成式AI原生设计原则，并提出生成式AI原生单元、有机底座、可编程路由器等架构模式，指导构建具有韧性和自我演进能力的系统。此外，我们还概述了生成式AI原生软件栈的关键要素，并从技术、用户采纳、经济与法律角度探讨其影响，强调需进一步验证与实验。本工作旨在激发未来研究，鼓励相关社区实施并完善该概念框架。
原文摘要 · Abstract (English)
Generative AI (GenAI) has emerged as a transformative technology, demonstrating remarkable capabilities across diverse application domains. However, GenAI faces several major challenges in developing reliable and efficient GenAI-empowered systems due to its unpredictability and inefficiency. This paper advocates for a paradigm shift: future GenAI-native systems should integrate GenAI's cognitive capabilities with traditional software engineering principles to create robust, adaptive, and efficient systems. We introduce foundational GenAI-native design principles centered around five key pillars -- reliability, excellence, evolvability, self-reliance, and assurance -- and propose architectural patterns such as GenAI-native cells, organic substrates, and programmable routers to guide the creation of resilient and self-evolving systems. Additionally, we outline the key ingredients of a GenAI-native software stack and discuss the impact of these systems from technical, user adoption, economic, and legal perspectives, underscoring the need for further validation and experimentation. Our work aims to inspire future research and encourage relevant communities to implement and refine this conceptual framework.
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