From Idea to Code with AI

Before touching AI, get clear on what you're building and for whom. Write this down — even a few sentences makes an enormous difference. This isn't just for AI; it's for you. Forcing yourself to articulate the problem clarifies your thinking.

A good problem definition answers three questions:

• What should the user be able to do? — The core use cases.
• What problem does this solve? — Why does this need to exist?
• What's the technical environment? — What stack, what constraints?

I want to build a family schedule planner in React.

Users should be able to:
- View a weekly calendar showing all family activities
- Add new activities (name, time, day, family member, color)
- Edit and delete existing activities
- Filter activities by family member

Technical environment:
- React with TypeScript
- Local state for now (no backend initially)
- Clean, modern UI with responsive design

Notice how this is precise without being exhaustive. It gives clear scope without prescribing implementation details. This leaves room for AI to contribute architectural ideas while staying on target.

This is where AI truly shines in the project planning phase. Instead of jumping to code, ask AI to design the system architecture first. You get a blueprint before a single line is written.

Based on this project description:

[paste your problem definition from Step 1]

Design the component architecture.

Show:
- Component tree (which components exist, how they nest)
- Props and state flow (where state lives, what gets passed down)
- TypeScript interfaces for the main data types
- File structure

Don't write implementation code yet — just the blueprint.

The response gives you a map of the entire project before you've written any code. You can evaluate the architecture, spot issues (too much nesting? wrong state placement? missing components?), and ask AI to revise before committing to implementation.

Now that you have an architecture, break the implementation into a sequence of milestones. Each milestone should be small enough to complete in one AI session and testable on its own.

Break this project into implementation milestones.

Each milestone should:
- Be completable independently
- Be testable (I can verify it works before moving on)
- Build on the previous milestone

Order them from simplest to most complex.
Include what to test at each milestone.

A typical result might look like this:

Each milestone is a clear, testable increment. You never move to the next milestone until the current one works correctly. This prevents the cascading-bug problem where broken foundations make everything above them unstable.

Now you implement — but one milestone at a time. Each prompt focuses on a single piece. This is where the architecture and breakdown from previous steps pay off enormously.

Implement Milestone 2: Static Calendar Grid.

Context:
- Using the TypeScript interfaces from Milestone 1
- WeekView component showing Monday—Sunday
- Each day is a column, hours 06:00—22:00 as rows
- CSS Grid layout, responsive (stacks on mobile)

Requirements:
- Functional component, TypeScript
- Clean, semantic HTML
- Time labels on the left
- Current day highlighted

Don't include activity display yet — just the empty grid.

See how specific this is? AI knows exactly what to build, what to include, and — critically — what not to include. The instruction "Don't include activity display yet" prevents scope creep, which is one of the most common problems with AI code generation.

After implementing a few milestones, step back and review the overall structure. Code written incrementally can drift — naming conventions might become inconsistent, patterns may diverge, or early design decisions may need revisiting.

Review the overall architecture of this project so far.

Here are the components:
[paste component code or file structure]

Questions:
- Is the component structure still clean?
- Are there any anti-patterns forming?
- Is state management in the right place?
- What would you improve before adding more features?

This review step is what separates projects that stay maintainable from ones that become tangled messes. Do it after every two or three milestones — the cost is five minutes; the benefit is avoiding a painful refactor later.