Building Apps from Scratch with AI: Lessons from a Failed Flutter Project

Mar 11, 2026

I wanted to build a Flutter app for the App Store. I had AI tools at my disposal, a detailed prompt, and a full development plan. What could go wrong?

Six hours later, my “orchestra of agents” had finished. I opened the simulator and saw… a single page crammed with standard buttons and a simply awful UX interface.

That’s not my story — it’s from a Reddit post in r/opencodeCLI. But it’s a story I’ve heard variations of dozens of times. The dream: describe your app, let AI build it. The reality: a mess that needs complete rebuilding.

Let me break down why this approach fails and what actually works.

The One-Shot Prompt Problem

Here’s what the Reddit user did:

1. Write detailed prompt describing entire app
2. AI creates development plan
3. Multi-agent system works autonomously for 6 hours
4. ???
5. Disappointment

It sounds reasonable on paper. AI is powerful, right? It can write code. Why not let it write a whole app?

The problem: AI excels at focused, small tasks. It struggles with large, unstructured problems.

Why One-Shot Prompts Fail

                    Context Window
┌─────────────────────────────────────────────┐
│  Your massive prompt (eats 30-50% already)  │
├─────────────────────────────────────────────┤
│                                             │
│     AI tries to hold entire app context     │
│     But it's like memorizing a novel        │
│     while trying to write a sequel          │
│                                             │
│     Details get lost. Decisions conflict.   │
│     Architecture becomes incoherent.        │
│                                             │
└─────────────────────────────────────────────┘

Issue	What Happens
Context overflow	AI “forgets” earlier decisions
No feedback loop	Mistakes compound without correction
UI/UX blindness	AI can’t see what it’s building
Integration gaps	Components don’t fit together
Inconsistent patterns	Different parts follow different conventions

The Reddit user got it right in their self-reflection: “I understand that 99.9% of the problem lies in my flawed approach to development.”

What AI Does Well vs What It Struggles With

Let me be clear: AI is genuinely useful for development. But you need to match the tool to the task.

AI Strengths

Task	Why It Works
Boilerplate code	Patterns are well-established
Known algorithms	AI has seen thousands of implementations
Writing tests	Clear input/output expectations
Refactoring	Local, well-defined scope
Documentation	Synthesizes existing code
Small features	Fits in context window

AI Weaknesses

Task	Why It Struggles
Visual design	No visual feedback loop
UX flow	Requires understanding user behavior
Architecture decisions	Needs holistic view of tradeoffs
Integration	Multiple moving parts
Coherent design	No “taste” or judgment
Large codebases	Context window limits

The Better Approach: Iterative Development

Here’s the workflow that actually works:

Phase 1: Foundation        Phase 2: Core Features      Phase 3: Polish
+---------------+         +---------------+          +---------------+
| Project setup |         | Feature 1     |          | Edge cases    |
| Architecture  |  ---->  | Feature 2     |  ---->   | Error states  |
| Data models   |         | Feature 3     |          | UI refinement |
+---------------+         +---------------+          +---------------+
       |                         |                          |
       v                         v                          v
   AI helps here            AI helps here             AI helps here
   (small tasks)            (one feature at time)     (incremental)

Phase 1: Foundation (Day 1-2)

Start with the boring stuff. AI is great at this.

Create a Flutter project structure for an expense tracking app:

1. Set up folder structure:
   - lib/models/
   - lib/screens/
   - lib/widgets/
   - lib/services/

2. Define the core data model:
   - Expense (id, amount, category, date, description)
   - Category (id, name, icon)

3. Set up the state management boilerplate
   (using Provider)

Keep each file under 100 lines. Use clear naming.

Phase 2: Feature by Feature (Day 3-10)

One feature per prompt. Verify before moving on.

Create a Flutter widget for an expense list item that shows:
- Expense amount (prominent, right-aligned)
- Category icon (left side, 24x24)
- Date (small, below description)
- Description (truncated to 2 lines)

Use this data model:
class Expense {
  final String id;
  final double amount;
  final String categoryIcon;
  final DateTime date;
  final String description;
}

Style: Clean, minimal, 16px padding horizontal.

Notice the difference? Specific, scoped, verifiable.

Phase 3: UI/UX Iteration (Day 11-15)

This is where most AI-first projects fail. UI requires visual iteration.

1. Get basic version working
2. Run it. Look at it. Hate it.
3. Identify ONE thing to improve:
   "The spacing between list items is too tight. Increase to 12px."
4. Verify fix
5. Repeat

AI cannot do this in one shot. Neither can you. Design is inherently iterative.

Prompt Engineering for App Development

Let me show you the difference between bad and good prompts:

Bad Prompt

- Nice UI
- Charts for spending
- Categories
- Cloud sync
- Export to CSV
Make it production-ready for the App Store.

This is asking for failure. It’s too vague (“nice UI”) and too broad (five major features).

Good Prompt

Fields:
- Amount (number input, required)
- Category (dropdown: Food, Transport, Entertainment, Bills, Other)
- Date (date picker, defaults to today)
- Description (text input, optional, max 200 chars)

Validation:
- Amount must be positive
- Category required
- Date cannot be future

State management: Use Provider
Form submission: Call onSubmit callback with Expense object

Total: ~150 lines max for the form widget.

Aspect	Bad Prompt	Good Prompt
Scope	Entire app	Single form
Specificity	”Nice UI”	Exact fields, validation
Constraints	None	150 lines max
Verifiability	Impossible	Easy to check

Here’s the workflow I’ve found effective:

Morning:
  - Review yesterday's work
  - Identify 2-3 small tasks for today
  - Write prompts before asking AI

Each Task:
  1. Write prompt (take 5 minutes to be specific)
  2. Get AI output
  3. Immediately test it
  4. Fix/refine if needed
  5. Commit working code

End of Day:
  - Run all tests
  - Manual testing of new features
  - Document any decisions made

Key Principles

Atomic tasks: Each prompt should produce testable output
Immediate verification: Don’t stack up untested code
Frequent commits: Working checkpoints let you roll back
Visual feedback: Run the app often for UI work
Human judgment: You decide what “good” looks like

What the Reddit User Should Have Done

Looking back at the original story, here’s what would have worked better:

Week 1: Foundation
  - Project setup (AI helps)
  - Data models (AI helps)
  - Basic navigation skeleton (AI helps)
  - Verify everything compiles and runs

Week 2: Core Feature
  - Single main feature (not all of them)
  - Build screen by screen
  - Test each screen before moving on
  - Commit working versions

Week 3: Secondary Features
  - Add remaining features one at a time
  - Each gets its own prompt and testing

Week 4: Polish
  - UI refinement (run app, look, adjust)
  - Error handling
  - Edge cases
  - Testing

Week 5: App Store Prep
  - Icons, splash screens
  - Metadata
  - Final testing

Notice: 5 weeks instead of 6 hours. But the result would be usable.

The Honest Truth

AI coding tools are powerful. They’ve changed how I work. But they’re not magic.

The Reddit poster wasn’t wrong to try. They were just using the wrong approach for the tool. It’s like using a hammer to build a house by hitting everything at once — technically the hammer can drive nails, but you need to place them one at a time.

The good news? Once you understand this, AI becomes genuinely helpful. I use it daily for:

Writing boilerplate (saves hours)
Generating tests (tedious work)
Refactoring (AI spots patterns I miss)
Documentation (I’d rather write code)

The key is treating AI as a collaborator on small tasks, not a contractor for entire projects.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Flutter Documentation
👨‍💻 OpenCode CLI
👨‍💻 Reddit Discussion

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!