Building a Mobile App with AI: A Journey from Concept to App Store

As artificial intelligence tools become increasingly sophisticated, developers are exploring new ways to leverage them in their workflow. I recently embarked on an interesting experiment: building a mobile application from scratch using AI assistance, specifically Cursor with Claude-3.5 Sonnet. Here's what I learned during this journey that led to a published app on the App Store.

Phase 1: Rapid Prototyping with AI

The initial development phase was surprisingly smooth. After a three-year break from React Native, I needed help getting back into the ecosystem.

Project Setup and Initial Development

Using Cursor's Composer feature, I started by providing a brief description of my desired application. The AI immediately generated a complete Expo project structure, handling all the necessary configurations and dependencies. This was particularly helpful as it eliminated the need to remember all the setup steps that might have changed during my three-year hiatus from React Native development.

Early Wins with AI

The initial iterations were remarkably successful. The AI-generated code compiled without errors, which was impressive considering the complexity of mobile development. Each feature request was met with coherent, working implementations. The AI showed a good understanding of React Native patterns and Expo's capabilities.

Rapid UI/UX Iteration

Perhaps the most surprising aspect was the speed of UI/UX iteration. Instead of spending hours in design tools like Figma, I could describe changes conversationally to the AI and see them implemented immediately. This phase took only 2 hours, during which I wrote zero code manually, focusing instead on refining the user experience through rapid iteration.

Phase 2: The Reality Check

As the project grew, several challenges emerged that revealed the limitations of AI-assisted development.

Code Quality Deterioration

The AI's inability to effectively refactor existing code became increasingly problematic. When pivoting features or making significant changes, the AI would often add new code without properly removing or updating the old implementation. This led to:

• Accumulation of dead code across multiple components
• Increasingly complex and intertwined component structures
• Reduced clarity in the codebase, making it harder for the AI to understand the context
• Duplicate functionality implemented in slightly different ways

Architectural Challenges

The lack of proper architecture became evident as the application grew:

1. Component Bloat: Components regularly exceeded 500 lines, mixing concerns and making maintenance difficult
2. State Management Chaos: Business logic was scattered across different layers without clear organization
3. Poor Separation of Concerns: Features were tightly coupled, making changes risky
4. Navigation Complexity: Screen flows became convoluted with multiple navigation patterns
5. Performance Issues: Unnecessary re-renders and inefficient data handling

The Cleanup Phase

The refactoring phase took significantly longer than the initial development - a full 5 hours of intensive code cleanup. This phase involved:

Code Restructuring

• Breaking down large components into smaller, focused ones
• Implementing proper state management patterns
• Establishing clear boundaries between business logic and UI
• Creating reusable components and hooks
• Standardizing navigation patterns

Technical Debt Resolution

• Identifying and removing unused code
• Fixing bugs introduced during multiple iterations
• Implementing proper error handling
• Optimizing performance bottlenecks
• Documenting key architectural decisions

Architecture Improvements

• Introducing proper dependency injection
• Implementing clean architecture principles
• Setting up proper testing infrastructure
• Creating clear data flow patterns
• Establishing coding standards and best practices

Key Learnings

AI Strengths

1. Rapid Prototyping: Unmatched speed in getting from concept to working prototype
2. Feature Exploration: Excellent for testing different approaches quickly
3. Boilerplate Generation: Efficient at creating standard patterns and structures
4. Documentation: Good at generating initial documentation and comments
5. Learning Tool: Helpful for understanding new frameworks or patterns

AI Limitations

1. Code Maintenance: Struggles with large-scale refactoring
2. Architecture: May not follow best practices for larger applications
3. Technical Debt: Can create maintenance challenges if not properly managed
4. Context Understanding: Difficulty maintaining consistency across multiple sessions
5. Performance Optimization: May not implement the most efficient solutions

Conclusion

AI tools like Cursor and Claude are incredibly powerful for prototyping and initial development, potentially replacing traditional design tools like Figma for quick proof-of-concepts. The experience resulted in a published app (CESU Planning on the App Store), demonstrating that AI-assisted development is viable but requires careful management.

Recommendations for Future Projects

1. Use AI primarily for initial prototyping and feature exploration
2. Take control of the codebase earlier in the development cycle
3. Implement strict architecture patterns from the start
4. Schedule regular human-led refactoring sessions
5. Use AI as a complementary tool rather than the primary developer
6. Document architectural decisions early and maintain them throughout
7. Set clear boundaries for AI involvement in different development phases

The future of AI in development is promising, but success lies in understanding both its capabilities and limitations. By leveraging AI's strengths while actively managing its limitations, developers can create successful applications more efficiently than ever before.