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

• URL: http://arxiv.org/abs/2306.12028v2
• Date: Wed, 20 Dec 2023 06:39:37 GMT
• Title: Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains
• Authors: Yu Cheng, Jieshan Chen, Qing Huang, Zhenchang Xing, Xiwei Xu and Qinghua Lu
• Abstract summary: 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. 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.
• Score: 31.080896878139402
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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