Background

Developing a Minimum Viable Product (MVP) swiftly is crucial for validating new ideas and attracting early investment. The challenge arises from the need to balance speed with maintaining a robust architecture, all while handling integration complexities and ensuring a seamless user experience.

We believe we can reframe software delivery from the ground up, where every decision, tool, and interaction is guided by contextual intelligence. — Cadabra Studio

What We Tried (and Why)

Given the tight timeline for creating a cross-platform assistant tool, we opted for a methodology called vibe-coding. This approach blends AI-assisted coding with a prompt-driven architecture, enabling rapid iterations and focus on high-level design over manual code writing. Initial efforts involved deploying a lightweight Flutter app integrated with Firebase and OpenAI’s API for speech and text transformation tasks.

What Broke or Didn’t Work

The primary challenge lay in AI’s occasional “hallucinations,” where it incorrectly guessed solutions, as well as the variability in AI-generated code quality. Many features required multiple prompt iterations before achieving desired outcomes.

📌 Takeaway: Always maintain manual oversight and verify AI-generated code.

The Shift We Made

To counteract AI’s limitations, we introduced stringent review processes, combined AI-generated outputs with traditional testing, and centralized architectural oversight to preserve system coherence.

What Worked (and What Still Doesn’t)

The vibe-coding approach enabled us to complete the MVP 60% faster than traditional methodologies without compromising core functionalities. However, it’s evident that AI-driven development requires human expertise for complex tasks like real-time operations and advanced concurrency management.

Tradeoffs and Strategic Decisions

Option A (Vibe-Coding) Option B (Traditional Development)
Faster development with AI assistance Established methodologies with predictable outcomes
Risk of AI misinterpretation; requires oversight More manual, potentially slower, but consistent quality
Smaller team, reduced costs Larger team for extensive manual coding

Open Questions We’re Still Exploring

  • How can AI improve real-time code optimization?
  • What’s the most effective balance between AI prompts and manual input?
  • How do we maintain code quality in the long term with AI-first approaches?

If You’re Solving Something Similar…

We encourage collaboration and the sharing of methodologies for using AI in rapid product development. If you’re facing similar challenges or exploring AI-augmented workflows, let’s connect and build better solutions together.

Contact: hello@cadabra.studio
More at: https://cadabra.studio

At Cadabra Studio, we believe we can reframe software delivery from the ground up, where every decision, tool, and interaction is guided by contextual intelligence.

👉 Read more in our extended article on Medium
👉 Explore applied insights on Cadabra Insights (Notion)

👉 Also read: Rebuilding an IT Company on AI: Insights from Cadabra Studio
👉 Related: Navigating AI Adoption: The Real Challenge is Team Psychology