Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native services, where natural language prompts serve as executable "code" directly (prompt as executable code), eliminating the need for programming language as an intermediary and opening up the door to personal AI. Prompt Sapper has emerged in response, committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence, unleashing the AI innovation potential of every individual, and forging a future where everyone can be a master of AI innovation. This article will introduce the R\&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices.

翻译：以GPT-4、DALL-E为代表的基础模型带来了前所未有的AI“操作系统”效应以及人机交互的新形式，催生了AI原生服务的创新浪潮。在这一浪潮中，自然语言提示词可直接作为可执行“代码”（提示词即代码），无需编程语言作为中介，开启了个人AI的大门。Prompt Sapper应运而生，致力于通过AI链工程支持AI原生服务的开发。它构建了一个基于大语言模型（LLM）的软件工程基础设施，通过人机协同智能实现AI链的创作，释放每个人的AI创新潜力，并塑造一个人人皆可为AI创新大师的未来。本文介绍了Prompt Sapper的研发动机，以及与之对应的AI链工程方法论与技术实践。