This draft paper presents a workflow for creating User Personas with Large Language Models, using the results of a Thematic Analysis of qualitative interviews. The proposed workflow uses improved prompting and a larger pool of Themes, compared to previous work conducted by the author for the same task. This is possible due to the capabilities of a recently released LLM which allows the processing of 16 thousand tokens (GPT3.5-Turbo-16k) and also due to the possibility to offer a refined prompting for the creation of Personas. The paper offers details of performing Phase 2 and 3 of Thematic Analysis, and then discusses the improved workflow for creating Personas. The paper also offers some reflections on the relationship between the proposed process and existing approaches to Personas such as the data-driven and qualitative Personas. Moreover, the paper offers reflections on the capacity of LLMs to capture user behaviours and personality traits, from the underlying dataset of qualitative interviews used for the analysis.

翻译：本稿提出一种利用大语言模型创建用户画像的工作流程，该流程基于对定性访谈进行主题分析的结果。与作者此前为同一任务开展的研究相比，本工作流程采用了改进的提示词技术及更庞大的主题库。这一突破得益于近期发布的一款支持处理16000个令牌的大语言模型（GPT3.5-Turbo-16k），同时归因于为画像创建过程提供的精细化提示词设计。本文详述了主题分析第二阶段与第三阶段的实施过程，继而探讨了创建用户画像的优化工作流程。此外，本文就所提流程与现有画像方法（如数据驱动画像与定性画像）之间的关系展开反思。文章进一步探讨了大语言模型从分析所用的定性访谈数据集中捕获用户行为与人格特征的能力。