Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

TL;DR

Prompt Sapper is a no-code development environment that leverages AI chain engineering principles to enable users to build reusable AI services with foundation models efficiently and correctly, reducing programming barriers.

Contribution

The paper introduces Prompt Sapper, a no-code IDE that applies software engineering principles to AI chain development, facilitating easier and more effective AI service creation.

Findings

User study shows improved efficiency in AI chain development.

Prompt Sapper enhances correctness and quality of AI chains.

Systematizes AI chain engineering with best practices.

Abstract

The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People can now use natural language (i.e. prompts) to communicate with AI to perform tasks. While people can use foundation models through chatbots (e.g., ChatGPT), chat, regardless of the capabilities of the underlying models, is not a production tool for building reusable AI services. APIs like LangChain allow for LLM-based application development but require substantial programming knowledge, thus posing a barrier. To mitigate this, we propose the concept of AI chain and introduce the best principles and practices that have been accumulated in software engineering for decades into AI chain engineering, to systematise AI chain engineering methodology. We also develop a no-code integrated development environment, Prompt Sapper, which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains, thereby improving the performance and quality of AI chains. With Prompt Sapper, AI chain engineers can compose prompt-based AI services on top of foundation models through chat-based requirement analysis and visual programming. Our user study evaluated and demonstrated the efficiency and correctness of Prompt Sapper.