I decided to test the promise that AI can dramatically speed up app development. Over several weeks I built small to medium features and one complete prototype using several AI coding tools. Below I summarize the approach I used, what worked, what didn’t, and practical tips for anyone who wants to try building apps with AI assistance.
Choosing the right tools
Picking the right AI tooling made a huge difference. I used a mix of code-completion models, snippet generators, and AI-assisted project scaffolding tools. Tools that integrated directly into my editor (autocomplete, inline suggestions, and context-aware snippets) were the most helpful for iterative development. Standalone tools that produced full files or project templates were useful for jump-starting structure but required more manual integration.
What worked:
- Editor-integrated completions for speeding up routine code (validation, state handling, simple UI components)
- Template generators to scaffold projects (React/Next, simple backend APIs)
What didn’t:
- Fully automated “build the app for me” features rarely produced production-ready code without extensive manual fixes
- Tools that ignored project context and dependencies created more rework than they saved
Rapid prototyping and scaffolding
AI excelled at creating an initial scaffold and boilerplate. I asked for Vibe Coding Agency project scaffold with authentication, routing, and a few example pages; in minutes I had a working skeleton. This let me focus on product logic and UX rather than wiring basic infrastructure.
Tips that worked:
- Start with a clear prompt describing tech stack, routing structure, and required pages
- Use generated code as a scaffold, not final code — refactor and harden before shipping
Implementing features with AI help
For discrete features (form validation, search filters, pagination), AI completions were hugely productive. I would write a short prompt or comment explaining desired behavior, and the model supplied component code, tests, and small utility functions.
What worked best:
- Small, well-scoped requests (one component or function at a time)
- Iterative refinement: generate → run → adjust prompt → regenerate
Pitfalls:
- Relying on AI for complex state management or performance-critical code often required rewriting
- Generated code sometimes included insecure patterns (e.g., unsanitized inputs) that needed human review
Backend and API development
AI helped accelerate API endpoint creation and simple business logic. For CRUD endpoints, authentication checks, and input validation, I saved time by asking the AI to generate handlers and request/response schemas.
What worked:
- Generating route handlers, TypeScript types, and basic tests
- Producing example database queries and migrations (useful as starting points)
What didn’t:
- Complex transactional logic, concurrency handling, and optimization still needed domain expertise
- Database schema design proposed by AI often required normalization and performance review
Testing and debugging
AI-assisted testing tools that produced unit tests and test data were surprisingly effective. Generating test cases for edge conditions saved time, and suggested fixes often highlighted pitfalls I missed.
What worked:
- Unit tests for isolated components and utility functions
- Property-based prompts to generate edge-case tests
Caveats:
- Tests generated by AI sometimes overfit the example behavior and missed real-world corner cases
- Debugging AI-generated code required tracing through layers of abstraction the model introduced
Collaboration and documentation
AI made it easier to produce documentation, README files, and code comments. That not only improved onboarding but helped me remember design decisions during later refactors.
Practical wins:
- Auto-generated README with setup and run instructions
- Inlined comments and docstrings produced from short prompts
Final verdict and practical advice
AI-driven coding accelerated routine work, scaffolding, and small-to-medium features. The tools that integrated into the editor and supported iterative prompting delivered the best ROI. However, AI is not a substitute for human architecture, security review, performance tuning, and product sense.
If you want to try building apps with AI:
- Use AI to scaffold and accelerate, not to fully replace developer judgment
- Keep prompts small and context-rich; iterate frequently
- Always review generated code for security, edge cases, and performance
- Pair AI with tests and CI so regressions don’t slip through
AI lowered the friction of getting from idea to prototype, but the last mile to a robust, secure, and maintainable app still requires experienced developers. Treat AI as a powerful assistant that speeds tasks, not an autopilot that can replace careful engineering.