Google Research

Prompt-based Prototyping with Large Language Models

ACM CHI case study track (2022)

Abstract

Prototyping is notoriously difficult to do with machine learning (ML), but recent advances in large language models may lower the barriers to people prototyping with ML, through the use of natural language prompts. This case study reports on the real-world experiences of industry professionals (e.g. designers, program managers, front-end developers) prototyping new ML-powered feature ideas via prompt-based prototyping. Through interviews with eleven practitioners during a three-week sprint and a workshop, we find that prompt-based prototyping reduced barriers of access by substantially broadening who can prototype with ML, sped up the prototyping process, and grounded communication between collaborators. Yet, it also introduced new challenges, such as the need to reverse-engineer prompt designs, source example data, and debug and evaluate prompt effectiveness. Taken together, this case study provides important implications that lay the groundwork toward a new future of prototyping with ML.

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