ChainBuddy: An AI Agent System for Generating LLM Pipelines

TL;DR

ChainBuddy is an AI assistant integrated into ChainForge that helps users generate and evaluate LLM pipelines from a single prompt, making the process more accessible and less demanding.

Contribution

This paper introduces ChainBuddy, a novel AI workflow generation assistant that simplifies creating LLM evaluation pipelines and demonstrates its effectiveness through user studies.

Findings

Participants reported lower workload and higher confidence with AI assistance.

Participants produced higher quality pipelines with AI assistance.

Experts rated workflows better when AI was used, revealing a subjective-objective performance mismatch.

Abstract

As large language models (LLMs) advance, their potential applications have grown significantly. However, it remains difficult to evaluate LLM behavior on user-defined tasks and craft effective pipelines to do so. Many users struggle with where to start, often referred to as the "blank page problem." ChainBuddy, an AI workflow generation assistant built into the ChainForge platform, aims to tackle this issue. From a single prompt or chat, ChainBuddy generates a starter evaluative LLM pipeline in ChainForge aligned to the user's requirements. ChainBuddy offers a straightforward and user-friendly way to plan and evaluate LLM behavior and make the process less daunting and more accessible across a wide range of possible tasks and use cases. We report a within-subjects user study comparing ChainBuddy to the baseline interface. We find that when using AI assistance, participants reported a less demanding workload, felt more confident, and produced higher quality pipelines evaluating LLM behavior. However, we also uncover a mismatch between subjective and objective ratings of performance: participants rated their successfulness similarly across conditions, while independent experts rated participant workflows significantly higher with AI assistance. Drawing connections to the Dunning-Kruger effect, we draw design implications for the future of workflow generation assistants to mitigate the risk of over-reliance.