From Throw-Away to Takeaway: How GenAI and Vibe Coding Accelerate Prototyping Across Technical Skill Levels

Paper Title

From Throw-Away to Takeaway: How GenAI and Vibe Coding Accelerate Prototyping Across Technical Skill Levels

Publication Info

• Topic area: Generative AI tools and their impact on digital product development across user skill levels.
• Keywords: Generative AI, vibe coding, prototyping, cloud development environments, technical expertise, democratization, product development, human-computer interaction, software engineering, skill acquisition.

Background and Problem

• Problem / challenge: While GenAI tools promise to democratize software development for non-technical users, there is limited understanding of how these tools are used across different stages of product development and by users with varying technical expertise.
• Significance: Understanding these dynamics is critical for improving tool design, supporting educational practices, and enabling broader participation in software creation.
• Motivation and related work: Previous research highlights the potential of GenAI tools in tasks like code generation and prototyping but has not comprehensively examined their use across the full product development lifecycle or the differences between technical and non-technical users.

Solution

• Proposed approach: A mixed-methods study combining an online survey (N = 85), hackathon interviews (N = 31), and practitioner interviews (N = 8) to analyze how technical and non-technical users employ GenAI tools across the product development lifecycle.
• Novelty:
  1. Comparative analysis of GenAI tool classes (chatbots, local development assistants, cloud development environments) across product development stages.
  2. Examination of differences in tool adoption and outcomes between technical and non-technical users.
  3. Insights into the benefits, challenges, and educational implications of GenAI-assisted workflows.
• Procedure and key techniques:
  • Conducted a survey to assess tool usefulness across seven product development stages.
  • Conducted semi-structured interviews with hackathon participants and practitioners to explore real-world tool usage and challenges.
  • Analyzed data using descriptive statistics, cumulative link mixed models, and qualitative coding aligned with the Double Diamond framework.

Results

• Concrete findings:
  • Chatbots were most useful in early stages like problem understanding (M = 3.98) and idea generation (M = 3.93).
  • Local development assistants (LDAs) were most effective during development (M = 4.47) and testing (M = 4.21).
  • Cloud development environments (CDEs) excelled in design and prototyping (M = 4.02) and were rated higher by non-technical users.
  • Non-technical users experienced steep learning curves and relied heavily on CDEs for prototyping, while technical users used LDAs for advanced development tasks.
• Advantage over baselines:
  • CDEs significantly accelerated prototyping, enabling rapid creation of high-fidelity artifacts for exploration and stakeholder engagement.
  • Non-technical users could independently create prototypes, though deployment and maintainability required technical expertise.
• Experiments / evaluation:
  • Survey: Assessed tool usefulness across seven stages of product development.
  • Hackathon interviews: Focused on early-stage prototyping and ideation.
  • Practitioner interviews: Explored deployment and monetization of GenAI-assisted products.
• Limitations and future work:
  • Limited diversity in practitioner interviews (N = 8, all male).
  • Findings reflect a snapshot of practices in 2025; longitudinal studies are needed to track evolving tool usage.
  • Further research is required to evaluate the quality and maintainability of AI-generated products and to explore the educational potential of GenAI tools.

Summary

This study investigates how GenAI tools and vibe coding practices impact digital product development across technical skill levels. Through a mixed-methods approach, it finds that CDEs enable non-technical users to rapidly prototype high-fidelity artifacts, while technical users benefit from LDAs for advanced development. However, deployment and maintainability remain challenging for non-technical users, highlighting the need for technical expertise. The findings suggest that GenAI tools redistribute programming expertise rather than replace it, emphasizing coordination and evaluation over routine coding. These insights inform tool design and educational strategies to support sustainable product development and broader participation in software creation.