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전규현 (Jeon gyuhyeon)
전규현 (Jeon gyuhyeon)

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WBS + AI: How to Cut Development Time by 6x

#wbs #aidevelopment #projectmanagement #chatgpt

"AI writes code well, but I'm uneasy about entrusting the entire project to it."

You're right. AI does well with small tasks, but struggles to understand and implement large projects as a whole.

But what if we combine it with WBS (Work Breakdown Structure)?

I'll share an experience where this combination cut development time by 6x in a recent project.

WBS + AI = Magic

Problem: Giving AI a Large Task Whole 

Request: "Make an e-commerce site"
AI: ???
Result: Useless skeleton code
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AI panics. It doesn't know where to start.

Solution: Break It Down with WBS First 

Request 1: "Make product list API (GET /products)"
AI: ✅ Perfect code generation

Request 2: "Make cart add API (POST /cart)"
AI: ✅ Perfect code generation

Request 3: "Make payment processing API (POST /payment)"
AI: ✅ Perfect code generation
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Each takes just 15 minutes!

Real Project: Login System

A real project I worked on recently.

Step 1: Break Down with WBS (30 minutes) 

Login System (Total 8 hours)
├── 1. Backend API (4 hours)
│   ├── 1.1 Signup API (1 hour)
│   ├── 1.2 Login API (1 hour)
│   ├── 1.3 Password Reset API (1 hour)
│   └── 1.4 Token Verification Middleware (1 hour)
├── 2. Database (1 hour)
│   ├── 2.1 Create User Table (30 min)
│   └── 2.2 Create Session Table (30 min)
├── 3. Frontend (2 hours)
│   ├── 3.1 Login Form Component (1 hour)
│   └── 3.2 Signup Form Component (1 hour)
└── 4. Testing (1 hour)
    ├── 4.1 API Integration Test (30 min)
    └── 4.2 E2E Test (30 min)
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Step 2: Add Specs to Each Task (1 hour) 

Task 1.1: Signup API
Time: 1 hour
Spec:
  Endpoint: POST /api/auth/register
  Input:
    email: string (RFC 5322)
    password: string (min 8, special characters)
    name: string
  Output:
    userId: UUID
    message: string
  Validation:
    - Email must be unique
    - Password strength check
  Security:
    - bcrypt (rounds: 10)
    - Rate limit: 3/hour/IP
  Error Cases:
    - 409: Email already exists
    - 400: Validation failed
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Step 3: Request AI Generation (2 hours)

For each task:

  1. AI generates code (5 min)
  2. Developer reviews (10 min)
  3. Request modifications (5 min)
  4. Add tests (10 min)

Result: Completed a 1-hour task in just 30 minutes!

Actual Measurement Results

Traditional Method

  • Full development: 80 hours (2 weeks)
  • Bug fixes: 40 hours (1 week)
  • Total 120 hours

WBS + AI Method

  • WBS creation: 2 hours
  • Spec writing: 4 hours
  • AI code generation: 8 hours
  • Review and modification: 6 hours
  • Total 20 hours

6x faster!

Even Faster with Parallel Processing

Hidden advantage of WBS: Parallel Processing

// Tasks without dependencies can be done simultaneously!
const parallelTasks = ['1.1 Signup API', '1.2 Login API', '2.1 User Table'];

// 3 people work with AI simultaneously
const results = await Promise.all(parallelTasks.map((task) => developer.workWithAI(task)));

// 3 hours of work in just 1 hour!
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A PM's Actual Day

How I actually work.

9 AM: Check Today's WBS (30 minutes) 

Today's tasks:
□ 1.1 Signup API
□ 1.2 Login API
□ 1.3 Profile API
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10 AM: First Task with AI (45 minutes) 

1. Copy spec → Paste to AI
2. Review generated code
3. "Add error handling here"
4. "Make test code too"
5. Done!
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11 AM: Second, Third Tasks (1 hour 30 minutes)

Repeat the same pattern. Gets faster each time.

2 PM: Integration Testing (1 hour) 

- Full API integration test
- Find bugs → Request fixes from AI
- Retest
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3 PM: Done!

Completed 8 hours of work in just 4 hours.

Practical WBS Writing Tips

1. Tasks in 1-2 Hour Units 

❌ Task too large
└── User Management System (40 hours)

✅ Appropriate size
├── Signup API (2 hours)
├── Login API (2 hours)
├── Profile Update API (1 hour)
└── Password Change API (1 hour)
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Reason: Size that AI can understand at once

2. Specify Dependencies 

Tasks:
  - id: 1.1
    name: 'DB Schema'

  - id: 1.2
    name: 'User Model'
    depends_on: [1.1] # 1.1 first!

  - id: 1.3
    name: 'Signup API'
    depends_on: [1.2] # 1.2 first!
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Reason: Prevents conflicts during parallel processing

3. Clear Completion Criteria 

Definition of Done:
  - [ ] API works confirmed
  - [ ] Error handling complete
  - [ ] Tests written
  - [ ] Documentation updated
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Reason: So neither AI nor humans get confused

Common Mistakes

Mistake 1: Task Too Large

Saying "Backend development" makes AI not know where to start.

Solution: Break down to maximum 2-hour units

Mistake 2: No Specs

Just saying "Login feature" makes AI create code like if (id == "admin").

Solution: Write specs in YAML or JSON

Mistake 3: Ignoring Dependencies

Making API before DB? You'll have to redo it later.

Solution: Draw dependency graph

Tool Selection Guide

WBS Management Tools

Essential Features:

  • Hierarchical structure support
  • Dependency management
  • Progress tracking
  • Spec attachment

Recommendations:

  • Plexo: WBS specialized, AI-friendly
  • Jira: Complex but powerful
  • Notion: Flexible but manual

AI Tools

Code Generation:

  • Claude: Long code, high accuracy
  • ChatGPT: Good at explanations, strong refactoring
  • Copilot: IDE integration, autocomplete

Real Project Application Cases

Case 1: Startup MVP (B2B SaaS)

  • Project: Team collaboration tool
  • Duration: 3 months → 3 weeks
  • Team: 3 people
  • WBS: 127 tasks
  • AI utilization: 70%
  • Result: 10x faster launch

Case 2: Legacy Migration

  • Project: PHP → Node.js
  • Duration: 6 months → 2 months
  • Team: 5 people
  • WBS: 342 tasks
  • AI utilization: 85%
  • Result: 50% fewer bugs

WBS + AI Synergy Effects

1. Predictability ↑

Breaking down into small units makes time prediction accurate. You can accurately answer "When will this feature be done?"

2. Quality Improvement ↑

Each task is small, so review is easy. Find bugs early, easily verify AI-generated code.

3. Stress ↓

The worry "Where do I start?" disappears. Just solve one by one from the top of the WBS.

4. Collaboration Efficiency ↑

Team members each take different WBS tasks and proceed in parallel. No conflicts, fast.

Conclusion: The Future is Already Here

WBS + AI isn't just a combination of tools. It's a new development paradigm.

In the future, developers will be:

  1. Breaking down problems with WBS
  2. Writing specs
  3. Collaborating with AI to implement
  4. Verifying quality

"AI Orchestrators"

Teams already working this way are building products 10x faster.

You should start before it's too late.

Divide and Conquer isn't just for algorithms. It's the core strategy of development in the AI era.

Need WBS-based project management and AI integration? Check out Plexo.

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