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REVIEW 3 major objections 4 minor 55 references

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

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A prompt-engineering approach to proto-persona generation, evaluated with 19 practitioners in a real Lean Inception, reduced creation time to about six minutes and was well accepted, but affective and behavioral empathy were only partially achieved.

desk verdict Useful, honest case study with a real speed claim that doesn't quite hold up—worth refereeing, but the 'days to under six minutes' framing needs to be fixed. read the letter →

arxiv 2507.08594 v1 pith:GLAYGQ2P submitted 2025-07-11 cs.SE cs.AIcs.HC

Fernando Ayach , Vitor Lameirão , Raul Leão , Jerfferson Felizardo , Rafael Sobrinho , Vanessa Borges , Patrícia Matsubara , Awdren Fontão This is my paper
classification cs.SEcs.AIcs.HC
keywords empathyproductproto-personasapproachdiscoveryefficiencyparticipantsacceptance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Proto-personas are lightweight user profiles teams create early in product development, before doing extensive user research. They help align the team on who the product is for. This study tested whether a set of structured prompts, used with ChatGPT 4o-mini, could help a team of 19 people generate these profiles during a real Lean Inception workshop for a government legal-tech product. Each participant used the same scripted prompt template, fed it the product vision and scope matrix, and recorded how long it took. On average the approach took about six minutes. Afterward they filled out questionnaires on acceptance and empathy and were interviewed. The team reported that the approach reduced time and effort, and they generally accepted the tool. They found the proto-personas useful as a starting point for discussion, especially because the personas felt realistic and matched the project context. However, participants noted that the personas sometimes sounded generic, lacked accessibility considerations, and did not always feel like they could empathize with them. Cognitive empathy, understanding the persona's situation, was strong, but affective empathy, feeling emotionally connected, and behavioral empathy, feeling moved to act, were weaker. The study is a single case without a control group, so the results are suggestive rather than conclusive. Future work could improve prompts, add accessibility requirements, and test the approach in more settings.
Extended reading notes

Core claim

The paper's central claim, stated in the Synthesis for RQ1, is: 'Our prompt-engineering approach cut proto-persona creation from days to under six minutes, automating repetitive steps and accelerating output.' If true, it demonstrates that a structured prompt workflow can make early-stage product discovery faster and more collaborative, with high user acceptance and strong cognitive empathy, though weaker affective and behavioral empathy.

Load-bearing premise

The approach's effectiveness depends on the assumption that the two input artifacts, Product Vision and the 'Is/Is Not/Does/Does Not' matrix, are sufficient and correctly transcribed to generate contextually accurate proto-personas. The paper checks transcription with a single reviewer (Section 4.4.2) but does not validate the quality or completeness of these artifacts. If these inputs are weak or incomplete, the generated personas will inherit those flaws, and the claimed 'Conformity of the Proto-personas with the Project Context' would not generalize.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a prompt-engineering-based approach for generating proto-personas during Lean Inception, refining a workflow previously proposed by the authors and evaluating it in a real software project with 19 participants. The empirical design combines timing data, TAM-based questionnaires, empathy questionnaires, and semi-structured interviews, with thematic synthesis used to answer four research questions on efficiency, effectiveness, acceptance, and empathy. The authors report that the approach is efficient, generally well accepted, and yields personas with strong cognitive empathy but weaker affective and behavioral empathy, and they provide an availability link to the study artifacts.

Significance. If the findings are taken as an exploratory qualitative case study, the paper makes a useful contribution: it embeds the prompt-engineering intervention in a real Lean Inception, uses multiple data sources, reports saturation, includes both technical and non-technical participants, and makes its instruments and prompts publicly available. The explicit attention to empathy as a multidimensional construct and the use of an Ishikawa diagram to link causes and effects are strengths. The main limitation is that the headline efficiency claim is broader than what the measurement actually supports, so the paper requires a careful reframing of that claim before its conclusions can be accepted.

major comments (3)
  1. [§5.0.1 (RQ1 Synthesis), §4.4.5, §4.5] The synthesis statement 'Our prompt-engineering approach cut proto-persona creation from days to under six minutes' is not supported by the measured data. The 5.94-minute average (SD 2.38) reported in §5.0.1 corresponds only to the individual execution of the pre-scripted prompts, as described in §4.4.5. The untimed activities include the LI convergence phase, in which the 19 participants collaboratively merged their outputs into final proto-personas, the preparation and transcription of the Product Vision and Is/Is Not matrix artifacts (§4.4.2), and any human review or refinement. In addition, the comparison in §4.5 is made against an external four-hour figure from [9], not against a measured manual baseline under comparable conditions. The paper should either redefine the claim as 'prompt execution takes under six minutes, while the full proto-persona process includes additional convergence time' or measure and report the total end-to-end time. This is load-bearing because the abstract and RQ1 conclusion currently present the unqualified 'days to under six minutes' as the main efficiency result.
  2. [§4.3, §5.0.2 (RQ2)] The effectiveness claim in the conclusion ('The approach proved efficient and effective') is stronger than what the evidence supports. The metric defined in §4.3 for RQ2 is the 'Effect of the generated proto-personas to the quality of MVP definition during Lean Inception,' but the analysis in §5.0.2 is based entirely on participant self-reports and researcher validation of the Ishikawa diagram. There is no objective measure of MVP quality, no independent evaluation of the generated personas, and no comparison against personas produced by a manual process. This is acceptable if the claim is explicitly framed as perceived effectiveness, but the current wording conflates perception with outcome. Please either soften the conclusion or add a complementary evaluation that does not rely solely on participants' impressions.
  3. [§4.4.2 and §5.0.2 (Conformity theme)] The study does not validate the quality or completeness of the two input artifacts, Product Vision and the Is/Is Not/Does/Does Not matrix, beyond a single reviewer checking that the transcription was faithful. The participants' positive assessments of contextual conformity, summarized in the theme 'Conformity of the Proto-personas with the Project Context,' therefore partly depend on the quality of these inputs rather than on the prompt-engineering approach itself. If the inputs are incomplete or biased, the generated personas inherit those flaws. The paper should state this dependency as a limitation and, ideally, include a brief content check of the input artifacts against the project's documentation or stakeholder knowledge.
minor comments (4)
  1. [§5.0.3] There are several typos and wording issues, including 'colaborate' instead of 'collaborate', 'dilema' instead of 'dilemma', and '5-Likert-Scale' instead of '5-point Likert scale.'
  2. [§5.0.1 (RQ1)] The text attributes a statement to 'Pereira et al. [19]', but reference [19] is Jackson et al.; please correct the citation or the name used in the text.
  3. [§4.4.5] The description of the execution phase says both that 'the execution was conducted by two researchers' and that 'each participant executed the approach separately'; please clarify who actually ran the prompts and who recorded the time, as this affects the interpretation of the 5.94-minute figure.
  4. [§5.0.4] For the empathy Likert results, the paper reports percentages (e.g., 32% neutral, 15% disagreement) without giving the exact counts. Since the sample has only 19 participants, reporting the raw counts alongside percentages would improve transparency and allow readers to judge the precision of the claims.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the empirical findings rest on independent participant feedback and measured execution times, with only a minor non-load-bearing self-citation.

full rationale

This paper is an empirical case study rather than a formal derivation, so the circularity patterns involving equations or fitted inputs do not apply. The prompt-engineering approach is described as 'a refined version of the method proposed by Leão et al. [24]' (Section 3), which is a self-citation, but the evidence for the reported results is independent of that prior work: RQ1 uses participant-reported times plus a measured average execution time of 5.94 minutes (Section 5.0.1), RQ2 and RQ3 use thematic synthesis of interviews and TAM Likert responses, and RQ4 uses empathy questionnaire responses. No parameter is fitted to a target result and then presented as a prediction; the efficiency comparison to a 'four hour session [9]' is an external benchmark, not a variable defined by the paper's own outputs. The fact that the convergence phase is untimed is a measurement validity concern, not a circularity. The only self-referential element is the provenance of the approach from the authors' prior work, which does not force any of the empirical findings; therefore, no circular step can be exhibited with a specific reduction.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The study introduces no free parameters or invented entities; it is a qualitative case study. The load-bearing assumptions concern the sufficiency of input artifacts, the validity of self-reported measures, and the interpretability of the execution-time number without a baseline.

assumptions (3)
  • domain assumption The two Lean Inception artifacts (Product Vision and the Is/Is Not/Does/Does Not matrix) are sufficient and accurate inputs for generating contextually appropriate proto-personas.
    The approach feeds these artifacts to the LLM and the paper's effectiveness claim depends on them adequately representing the product scope. The paper only checks transcription with one reviewer (Section 4.4.2), not the quality of the artifacts themselves.
  • domain assumption Self-reported perceptions via TAM and empathy Likert scales measure the intended constructs of acceptance and empathy.
    All acceptance and empathy metrics are based on participants' self-reports rather than objective behavioral measures. This is a standard but unvalidated assumption in survey-based research.
  • domain assumption The execution time of the approach (average 5.94 minutes) is a meaningful measure of efficiency even though no manual baseline was measured in the same context.
    The paper compares this number to participants' statements about manual days of work, but does not directly measure the alternative. This assumption underlies the RQ1 efficiency claim.

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Cite this review

Pith. "Pith review of Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy." pith.science (2026). https://pith.science/paper/GLAYGQ2P

@misc{pith2026250708594,
  author       = {Pith},
  title        = {Pith review of: Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GLAYGQ2P}},
  note         = {Machine review of arXiv:2507.08594}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2507.08594 by the authors.

Figure 1
Figure 1. Refined approach based on Leão et al. [24] [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Summarized Ishikawa Diagram Effect 1: Motivation to make adaptations to the product to meet certain proto-personas. Developing empathy with the proto-personas (not necessarily using AI) is a factor that contributes to the motivation to help them [22]. The theme Factors that fa￾cilitated the development of empathy with proto-personas reinforces this aspect. P7 expresses it: “What contributes most to identification is… view at source ↗

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