Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy

TL;DR

This study explores using prompt engineering with Generative AI to efficiently create proto-personas during early product discovery, aiming to improve quality, reduce effort, and understand empathy impacts.

Contribution

It provides empirical evidence on the effectiveness and challenges of integrating GenAI into proto-persona generation in real-world product discovery.

Findings

Reduced time and effort in persona creation

Improved quality and reusability of personas

High acceptance and perceived usefulness

Abstract

Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.