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

arXiv: https://arxiv.org/abs/2507.08594 · HTML: https://mlsanity.com/paper/2507.08594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations](https://mlsanity.com/paper/2311.03999/) (2023)
- [Who Uses Personas in Requirements Engineering: The Practitioners' Perspective](https://mlsanity.com/paper/2403.15917/) (2024)
- [Enablers and Barriers of Empathy in Software Developer and User Interaction: A Mixed Methods Case Study](https://mlsanity.com/paper/2401.09001/) (2024)
- [ExplainitAI: When do we trust artificial intelligence? The influence of content and explainability in a cross-cultural comparison](https://mlsanity.com/paper/2503.17158/) (2025)
- [Empathy Models and Software Engineering -- A Preliminary Analysis and Taxonomy](https://mlsanity.com/paper/2305.03941/) (2023)
- [Investigating the Developer eXperience of LGBTQIAPN+ People in Agile Teams](https://mlsanity.com/paper/2501.13257/) (2025)

## Abstract (arXiv)

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.