[Paper Review] Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services

Zhenchang Xing, Qing Huang+4 more|arXiv (Cornell University)|Jun 4, 2023

Scientific Computing and Data Management4 citations

TL;DR

Prompt Sapper introduces an LLM-empowered software engineering infrastructure that enables non-technical users to author, compose, and deploy AI-native services through natural language prompts, using AI chain engineering to structure workflows. It lowers the barrier to AI development by integrating virtual AI assistants (product manager, architect, prompt engineer) and a purpose-built IDE, enabling end-to-end, human-AI collaborative development of customizable, reusable AI services without traditional coding.

ABSTRACT

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.

Motivation & Objective

• Address the gap in software engineering infrastructure for AI-native services, where foundation models enable 'prompt as code' but lack systematic development support.
• Overcome the limitations of chat-based interaction and ad-hoc prompt engineering by introducing a structured, software-engineering-driven approach to AI service creation.
• Democratize AI innovation by enabling non-programmers to design, prototype, and deploy personalized AI services through intuitive, natural language-driven workflows.
• Establish a holistic AI4SE4AI framework that integrates AI and software engineering best practices to improve development efficiency, reusability, and maintainability of AI chains.
• Bridge the gap between AI capability and real-world deployment by providing a full-stack, interactive environment for designing, testing, and sharing AI-native services.

Proposed method

• Leverages foundation models as an AI 'operating system' and uses 'prompt as code' to directly execute user intent without traditional programming.
• Introduces AI chain engineering as a novel software paradigm that composes multiple prompt calls to foundation models, external APIs, and traditional AI models into structured, executable workflows.
• Employs LLM-powered virtual assistants (product manager, architect, prompt engineer) to guide users through requirements analysis, task decomposition, and prompt construction.
• Provides a purpose-built Sapper IDE with a user-friendly interface that supports real-time collaboration between humans and AI, enabling intuitive prototyping and iteration.
• Applies software engineering principles—such as modularity, versioning, and testing—to AI chains to improve maintainability, reusability, and reliability.
• Integrates the AI4SE4AI framework to systematize the entire lifecycle of AI service development, from requirements to deployment, with support for continuous improvement and service composition.

Experimental results

Research questions

• RQ1 How can we create a scalable, user-friendly infrastructure that enables non-technical users to author and deploy AI-native services using only natural language?
• RQ2 What role can LLM-powered virtual assistants play in guiding non-experts through the complex process of AI chain design and prompt engineering?
• RQ3 How can software engineering best practices be adapted to the unique challenges of AI-native service development, particularly in terms of testing, reuse, and maintainability?
• RQ4 In what ways does AI chain engineering outperform existing low-code/no-code tools and traditional IDEs in supporting the full lifecycle of AI service development?
• RQ5 Can a prompt-as-code paradigm, when combined with structured AI chain engineering, achieve both democratization of AI development and production-grade reliability?

Key findings

• Prompt Sapper enables non-technical users to create, customize, and compose AI-native services through natural language prompts, effectively eliminating the need for traditional programming.
• The integration of LLM-powered virtual assistants significantly reduces the cognitive load and technical barriers for users during requirements analysis and prompt construction.
• The Sapper IDE supports end-to-end development of AI chains with a focus on usability, collaboration, and maintainability, outperforming existing tools that focus only on coding or visual workflow design.
• By applying software engineering principles to AI chains, Prompt Sapper enhances reusability, testability, and version control—key enablers for scalable AI service development.
• The AI4SE4AI framework demonstrates that systematic engineering practices can be successfully adapted to AI-native development, enabling robust, production-ready AI services.
• Compared to tools like GitHub Copilot or Zapier, Prompt Sapper supports a more holistic, intention-driven development process that spans from idea to deployment, not just code generation.