The Modern AI Coding Workflow - How Top Developers Ship Faster

AI hasn't just added new tools—it's transformed how the best developers work. Here's the complete workflow used by teams shipping 10x faster.

---

Table of Contents

1. The New Development Cycle
2. Phase 1: Planning with AI
3. Phase 2: Building with AI
4. Phase 3: Testing & Debugging
5. Phase 4: Review & Deploy
6. Daily Workflow Example
7. Tool Stack Recommendations

---

The New Development Cycle

Traditional Workflow

1. Requirements → 2. Design → 3. Code → 4. Test → 5. Deploy
                    ↑__________________________________|
                              (Bugs/Changes)

AI-Enhanced Workflow

1. Requirements + AI Planning
       ↓
2. AI-Assisted Design/Prototyping
       ↓
3. AI Code Generation + Human Refinement
       ↓
4. AI-Assisted Testing + Debugging
       ↓
5. AI Code Review + Deploy
       ↓
6. AI Monitoring + Iteration

Key difference: AI at every step, human as director.

The 80/20 of AI in Coding

• 80% of boilerplate can be AI-generated
• 20% is nuanced work requiring human judgment
• Focus your energy on the 20%

---

Phase 1: Planning with AI

Step 1: Requirements Clarification

Use Claude or ChatGPT to refine vague requirements:

I'm building a feature for [context].

The requirement from stakeholders is:
"We need a way for users to export their data"

Please help me:
1. Identify clarifying questions I should ask
2. List potential edge cases
3. Suggest a scope for an MVP version
4. Identify technical considerations

Step 2: Technical Specification

Generate a technical spec:

Based on this requirement: [requirement]

For our stack: [tech stack]

Please create a technical specification including:
1. System architecture overview
2. Data models needed
3. API endpoints
4. Component breakdown
5. Dependencies required
6. Estimated complexity (hours)

Step 3: Task Breakdown

Get actionable tasks:

Break this feature into development tasks:
- Each task should be completable in 2-4 hours
- Include clear acceptance criteria
- Note dependencies between tasks
- Suggest implementation order

Planning AI Tools

• Claude: Best for complex reasoning
• ChatGPT: Good for brainstorming
• Notion AI: Integration with project management
• Linear AI: Task generation (coming)

---

Phase 2: Building with AI

Step 1: Project Setup

Use AI for scaffolding:

Create the project structure for a [type] application using:
- Next.js 14 App Router
- TypeScript
- Tailwind CSS
- [other dependencies]

Include:
- Folder structure
- Config files
- Base components
- Type definitions

In Cursor: Use Composer for multi-file setup. In Bolt.new: Describe and generate entire project.

Step 2: Core Implementation

Workflow:

1. Start with types/interfaces

Create TypeScript interfaces for:
- User data model
- API responses
- Component props

2. Generate base components

Create a data table component with:
- Sorting
- Filtering
- Pagination
- Loading states

3. Add logic incrementally

Add to this component:
- API integration using our fetch wrapper
- Error handling
- Caching with React Query

4. Refine and polish

Improve this component:
- Add accessibility (ARIA labels)
- Add keyboard navigation
- Optimize re-renders

Step 3: Integration

Connect pieces together:

I have these components:
[paste component A]
[paste component B]

I need to:
1. Have A pass data to B
2. Handle loading states
3. Manage shared state

Show me the integration code.

Building AI Tools

• Cursor: Primary IDE (Cmd+K, Composer)
• Copilot: Real-time autocomplete
• v0: UI component generation
• Claude.ai: Complex logic discussions

---

Phase 3: Testing & Debugging

AI-Assisted Testing

Generate test cases:

For this function:
[paste function]

Generate comprehensive tests covering:
- Happy path
- Edge cases
- Error scenarios
- Boundary conditions

Use Jest and React Testing Library.

Test coverage analysis:

Here are my tests:
[paste tests]

What scenarios am I missing?
What edge cases could break this?

AI Debugging Workflow

When you hit an error:

1. Capture context

   • Error message
   • Stack trace
   • Relevant code
   • Recent changes

2. Ask AI

I'm getting this error:
[error]

In this code:
[code]

After making this change:
[what you did]

Expected behavior:
[what should happen]

What's wrong and how do I fix it?

3. Apply fix + verify

4. Learn

Explain why this error occurred so I can 
avoid it in the future.

Complex Debugging

For mysterious bugs:

This bug has me stuck. Here's everything I know:

Symptoms:
[describe behavior]

When it happens:
[trigger conditions]

When it doesn't:
[working scenarios]

What I've tried:
[attempted solutions]

Relevant code:
[paste code]

Please help me:
1. Form hypotheses about root cause
2. Suggest debugging steps to narrow down
3. Propose potential fixes

Testing & Debugging Tools

• Cursor Chat: Quick debugging
• Claude: Complex issue analysis
• Codium AI: Test generation
• Sentry AI: Production error analysis

---

Phase 4: Review & Deploy

AI Code Review

Before committing:

Review this code for:
- Bugs and logical errors
- Performance issues
- Security vulnerabilities
- Best practice violations
- Opportunities to simplify

[paste code]

Be critical—I want to catch issues before merge.

Pre-Deployment Checklist

I'm about to deploy this feature:
[describe feature]

Create a deployment checklist including:
- Tests to verify
- Manual checks needed
- Rollback procedures
- Monitoring to add
- Feature flags if needed

Documentation Generation

Generate documentation for:
[paste code]

Include:
- Overview/purpose
- API reference
- Usage examples
- Configuration options
- Common issues

Review & Deploy Tools

• Maige: AI PR review
• CodeRabbit: Automated review
• Mintlify: Documentation generation
• Cursor: Final code polish

---

Daily Workflow Example

Morning (Planning)

9:00 AM - Review tasks

I'm working on [feature] today.
Here's the context: [context]

Help me:
1. Prioritize what to tackle first
2. Identify potential blockers
3. Estimate time for each task

9:30 AM - Design session Use v0 or Claude to sketch UI/architecture.

Midday (Building)

10:00 AM - 12:00 PM - Core implementation

• Cursor Composer for multi-file creation
• Copilot for inline completion
• Cmd+K for targeted edits

12:00 PM - Quick debug Any blockers? AI chat for quick unblocking.

Afternoon (Refinement)

1:00 PM - 3:00 PM - Continue implementation

• More complex logic
• Edge case handling
• Error states

3:00 PM - Testing

• Generate tests with AI
• Run and fix failures
• AI helps debug issues

End of Day (Review)

4:00 PM - Code review

Review today's changes:
[paste diff]

Anything I should fix before committing?

4:30 PM - Documentation

Update documentation for these changes:
[describe changes]

5:00 PM - Tomorrow's prep

I got to [point] today.
Tomorrow I need to: [remaining work]

Any prep I should do now to make 
tomorrow smoother?

---

Tool Stack Recommendations

Minimum Viable Stack

For solo developers:

Total: $0

Professional Stack

For serious builders:

Total: $40-190/mo

Team Stack

For startups:

Total: ~$65/user/mo + API

---

Workflow Optimization Tips

Tip 1: Create Reusable Prompts

Save prompts that work:

# My Prompts

## New Component
Create a React component for [X] with:
- TypeScript props interface
- Tailwind styling
- Loading and error states
- Accessibility attributes

## Debug Template
Error: [error]
Code: [code]
Expected: [expected]
Tried: [attempts]

Tip 2: Use .cursorrules

Project-specific AI instructions:

# Always:
- Use TypeScript strict mode
- Follow existing patterns
- Add error handling
- Include types

# Never:
- Use any type
- Skip loading states
- Create new patterns when existing ones work

Tip 3: Context Windows are Your Friend

Claude's 200K context means you can:

• Share entire file systems
• Include documentation
• Provide extensive examples

Don't skimp on context.

Tip 4: Learn Keyboard Shortcuts

Cursor:

• Cmd+K: Inline edit
• Cmd+L: Open chat
• Cmd+Shift+K: Composer
• @file: Reference file

Speed compounds.

Tip 5: Daily Retro

End each day:

What AI workflows worked well today?
What was frustrating?
What could I do differently tomorrow?

Continuous improvement.

---

Common Workflow Mistakes

Mistake 1: Not Iterating

Expecting perfection on first prompt. Instead: build up incrementally.

Mistake 2: Too Much AI

Letting AI make all decisions. You should: understand every line.

Mistake 3: Not Enough AI

Doing things manually that AI does well. Automate: boilerplate, tests, docs.

Mistake 4: Poor Context

Giving AI minimal information. Better: thorough context = better output.

Mistake 5: No Verification

Trusting AI blindly. Always: test, review, understand.

---

Conclusion

The modern AI coding workflow isn't about replacing thinking—it's about amplifying it.

Key principles:

1. AI at every phase (plan → build → test → deploy)
2. Human as director, AI as executor
3. Iterate quickly, refine continuously
4. Always understand what AI produces
5. Optimize your tools and prompts

Master this workflow, and you'll ship faster than ever before.

---

Want help implementing this workflow? DreamLaunch uses AI-powered development to ship MVPs in 28 days. Book a free consultation to discuss your project.