Original Paper: https://arxiv.org/abs/2306.02230

By: Zhenchang Xing, Qing Huang, Yu Cheng, Liming Zhu, Qinghua Lu, Xiwei Xu

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.

Summary Notes

Revolutionizing AI Service Development: A Closer Look at Prompt Sapper and AI Chain Engineering

The rapid advancement of artificial intelligence (AI), marked by game-changing foundation models like GPT-4 and DALL-E, is reshaping our interaction with technology.

These developments are ushering in a new wave of software engineering, characterized by the use of natural language prompts as executable code. This blog explores the innovative approach of Prompt Sapper and AI chain engineering, offering a detailed guide for AI Engineers in enterprises.

Introduction

From Past to Present

The transition from the era of PCs and smartphones to today's AI-driven technologies highlights a significant shift towards making human-AI interactions more intuitive and straightforward, thanks to foundation models.

The Current Landscape

Today's AI technologies, led by foundation models, are streamlining AI service development and deployment. Despite these advances, the dependency on traditional software development poses challenges to AI's wider adoption.

Challenges Ahead

The need for traditional programming skills remains a major barrier, restricting the pool of individuals who can develop and innovate with AI. This limitation hampers the speed of AI service development and deployment.

AI Chain Engineering: The Future of Software

The Vision

AI chain engineering aims to transform software development through generative AI, using foundation models as AI-operating systems to assemble prompt calls into comprehensive services.

Approach

• Utilizing AI chains to simplify the creation of AI-native services, focusing on task workflows and data characteristics.
• Emphasizing the strategic design of prompts to effectively use AI capabilities.

Key Components

• AI Chain Builders: Empower individuals to create AI services using natural language.
• Software 3.0: A shift towards focusing on tasks and data, minimizing traditional coding complexities.

Promptmanship: Mastering AI Prompts

The Concept

Adapting software engineering for AI-native development, focusing on designing prompts that maximize AI's potential.

LLM Integration

Incorporating Large Language Models in the development process enhances various stages, from analysis to design, for a smoother experience.

Understanding AI

Knowing the strengths and limitations of LLMs is essential for creating efficient and reliable AI services.

Sapper IDE: Simplifying Development

Purpose

The Sapper IDE makes developing AI chains accessible to non-technical users, democratizing AI service creation.

Features

• Human-centric Design: Simplifies interaction and service creation.
• AI Co-pilots: Provides AI assistance throughout the development process.
• No-code Development: Supports a no-code approach from design to deployment.

AI Services Marketplace: Sharing and Trading AI Services

Goal

To create a platform for exchanging AI-native services, enhancing the ecosystem with a diverse range of solutions.

Unique Value

This marketplace specializes in prompt-driven services and AI capabilities, setting it apart from traditional code repositories.

Community Involvement

The platform encourages contributions from the community, enriching the AI services ecosystem.

Exploring Related Work

• AI Chain Applications: Examines successful uses of AI chains.
• Human-AI Collaboration: Highlights collaborative approaches in AI service development.
• Enhancing Accessibility: Discusses reducing the technical skill barrier in AI development.

Conclusion

Prompt Sapper and AI chain engineering mark a significant evolution in AI service development and deployment.

By making AI service creation more accessible, these innovations could significantly expand AI's integration into society and the economy.

Acknowledgments

We extend our gratitude to the collaborators and institutions behind the Prompt Sapper's research and development.

For AI Engineers at enterprise companies, embracing Prompt Sapper and AI chain engineering means leading the way in creating innovative, efficient, and accessible AI-native services.

This revolution in software development not only paves the way for new innovation opportunities but also drives growth in the AI industry.