PromptChainer: Chaining Large Language Model Prompts through Visual Programming

TL;DR

PromptChainer introduces a visual programming interface that simplifies the creation and debugging of complex LLM chains, making AI prototyping more accessible for non-experts.

Contribution

This work presents PromptChainer, a novel interactive tool that enables visual authoring and debugging of LLM chains, addressing key challenges identified in user needs.

Findings

Supports rapid prototyping of diverse applications

Enhances transparency and control in chain creation

Facilitates debugging at multiple granularities

Abstract

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.