PromptChainer: Chaining Large Language Model Prompts through Visual Programming

• DOI: 10.1145/3491101.3519729
• Publication Date: 2022-04-29T16:49:48Z
• Authors: Tongshuang Wu; Ellen Jiang; Aaron Donsbach; Jeff Gray; Alejandra Molina; Michael Terry; Carrie J Cai
• Keywords: FOS: Computer and information sciences; Computer Science - Human-Computer Interaction; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Human-Computer Interaction (cs.HC)

CHI LBW 2022<br/>While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Recent work has found that chaining multiple LLM runs together (with the output of one step being the input to the next) can help users accomplish these more complex tasks, and in a way that is perceived to be more transparent and controllable. However, it remains unknown what users need when authoring their own LLM chains -- a key step for lowering the barriers for non-AI-experts to prototype AI-infused applications. In this work, we explore the LLM chain authoring process. We conclude from pilot studies find that chaining requires careful scaffolding for transforming intermediate node outputs, as well as debugging the chain at multiple granularities; to help with these needs, we designed PromptChainer, an interactive interface for visually programming chains. Through case studies with four people, we show that PromptChainer supports building prototypes for a range of applications, and conclude with open questions on scaling chains to complex tasks, and supporting low-fi chain prototyping.<br/>