AI based MVP Development with Vibe Coding

Initial Situation

This project was set up as a learning experiment to build fast, effective MVPs.

The goal was to create an MVP for assessing energy efficiency and renovation potential of residential buildings in the DACH region, focusing on rapid business validation and solid product logic rather than technical perfection. In parallel, the project explored hands-on integration of data models, AI APIs (ChatGPT), and vibe-coding workflows to accelerate learning and iteration.

Approach

The project was deliberately structured as a vibe-coding initiative, prioritizing business requirements over technical tooling.

Product decisions came first; implementation details followed.

V0 was used to rapidly generate and iterate on UI components, while Vercel enabled fast deployment, continuous iteration, and tight feedback loops—allowing the product to evolve directly from real usage and insights.

Product development

• Translation of clearly defined business requirements (logic, clarity, comprehensibility) into concrete product features
• Development of a functional MVP with iterative refinement of UI, logic, and data model
• Use of advanced prompting: JSON-based prompts with strictly defined output structures
• Direct integration of AI output (JSON) back into the product for structured result presentation
• Controlled AI recommendations driven by prompt logic rather than uncontrolled free-text outputs

Key Learnings

• Vibe coding is extremely efficient when business logic and objectives are clearly defined
• Documenting each step during vibe coding is essential to later understand when and why features were introduced
• MVPs are decision-making tools—not mini end products
• Tools like V0 and Vercel accelerate execution but do not replace a product team
• UX quality improves through testing within the live product
• Data models are critical: generic LLMs without a use-case-specific data foundation hallucinate quickly and undermine product credibility
• Advanced prompting is mandatory: structured JSON outputs make AI results verifiable, UI-ready, and production-oriented
• AI recommendations must be constrained, explainable, and controllable
• AI-generated results must be continuously evaluated against real-world data to improve accuracy, credibility, and decision quality over time

Current Status & Open Development

The currently available online version is intentionally positioned as an MVP and is not final. The following areas are being developed iteratively:

• Refinement of the data model to reduce uncertainty and improve assessment quality
• Improvement of decision logic, particularly the clear separation of when the AI decides and when human judgment is required
• Refinement of UI elements, including accessibility, clarity, and consistent interaction patterns

Outcome

A robust MVP showing how vibe coding, structured AI prompting, and clean data models can be combined to build a credible AI product. The MVP serves as a foundation for usability testing, iterative improvement of the scoring model, validation against real-world buildings, and the exploration of business models.