Iterative Development: The Vibe Loop

The single most important skill in vibecoding. If you read one guide, make it this one.

If there’s one concept that defines vibecoding, it’s the loop: prompt, generate, review, refine. This rhythm is the heartbeat of AI-assisted development. Master it, and you’ll build faster without sacrificing quality.

What you’ll learn:

• The 4-phase loop (and why 2-5 iterations is normal)
• How to review AI output critically
• When to ship vs. when to refine
• Copy-paste prompts for each phase

The Vibe Loop Visualized

        ┌─────────────┐
        │   PROMPT    │
        │  Describe   │
        │  intent     │
        └──────┬──────┘
               │
               ▼
        ┌─────────────┐
        │  GENERATE   │
        │  AI writes  │
        │  code       │
        └──────┬──────┘
               │
               ▼
        ┌─────────────┐
        │   REVIEW    │
        │  Evaluate   │
        │  critically │
        └──────┬──────┘
               │
      ┌────────┴────────┐
      │                 │
      ▼                 ▼
  ┌───────┐       ┌─────────┐
  │ SHIP! │       │ REFINE  │
  │ Done  │       │ Improve │
  └───────┘       └────┬────┘
                       │
                       └───► Back to PROMPT

Most features take 2-5 loops. This is normal—not a failure.

Phase 1: Prompt

Express your intent clearly. Good prompts are:

Quality	Description	Example
Specific	Clear outcome	”Validate email format using RFC 5322”
Contextual	Relevant background	”Using our existing Zod schemas”
Bounded	Reasonable scope	One function, not an entire system
Open	Room for AI knowledge	Let AI choose implementation details

First Prompt Examples

✅ Good:

Create a function that validates user registration data:
- Email: valid format
- Password: 8+ chars, 1 uppercase, 1 number
- Username: 3-20 chars, alphanumeric only

Return { valid: boolean, errors: string[] }
TypeScript, with Zod for validation.

❌ Too vague:

Make a validator

❌ Too large:

Create a complete user registration system with frontend forms,
backend validation, database storage, email verification, admin
dashboard, password reset, and audit logging.

Tool-Specific Prompt Patterns

Different tools work best with different prompt styles:

Claude Code (Terminal)

Look at src/lib/validators.ts and create a new validateRegistration
function following the same patterns. Use Zod. Handle email, password,
and username validation.

Claude Code reads your files, so reference existing patterns directly.

Cursor (IDE) Select existing code, press Cmd+K:

Create a similar validation function for registration data.
Add email, password (8+ chars, 1 uppercase, 1 number), and username.

Cursor uses selection as context, so highlight relevant examples.

Copilot Chat

@workspace Create a registration validator in src/lib/validators.ts
following our existing Zod patterns. Include email, password, username.

Use @workspace to give Copilot project context.

Phase 2: Review

This is where your judgment matters most. Never blindly accept AI output.

The Review Checklist

□ CORRECTNESS
  □ Does it do what I asked?
  □ Are edge cases handled?
  □ Any obvious bugs?

□ QUALITY
  □ Is it readable?
  □ Follows project patterns?
  □ Performance concerns?

□ COMPLETENESS
  □ Anything missing?
  □ Error handling?
  □ Types correct?

□ INTEGRATION
  □ Works with existing code?
  □ Naming consistent?
  □ Imports correct?

Review Example

AI generates:

function validateEmail(email: string): boolean {
  const regex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return regex.test(email);
}

Your review:

• ✅ Basic structure good
• ✅ Simple regex, catches most cases
• ⚠️ No max length check (RFC 5321: 254 chars)
• ⚠️ Returns boolean, but I asked for { valid, errors }
• ❌ No TypeScript generics or proper return type
• ❌ Doesn’t use Zod like I specified

This review informs your refinement.

Phase 3: Refine

Guide the AI toward better output. Be specific:

Vague refinement (less effective):

Make it better

Specific refinement (more effective):

Good start. Refinements:
1. Use Zod instead of raw regex
2. Return { valid: boolean, errors: string[] } not just boolean
3. Add max length check (254 chars per RFC 5321)
4. Add JSDoc comment explaining the validation rules

The AI incorporates feedback. You review again. Loop continues.

Loop Velocity: When to Go Fast vs. Slow

Fast Loops 🏃‍♂️

For exploration, boilerplate, non-critical code:

Generate a basic loading spinner component

Quick review. Roughly right? Move on. Refine later if needed.

Careful Loops 🔍

For security, data integrity, complex logic:

Create a function to process Stripe payment webhooks.
Must:
- Verify webhook signature
- Be idempotent (handle duplicates)
- Log everything for debugging
- Handle partial failures gracefully

Thorough review. Test edge cases. Ask follow-ups:

What happens if signature verification throws?
How is idempotency guaranteed if the process crashes mid-execution?
Show me the test cases you'd write for this.

Loop Speed Guide

Situation	Speed	Review Depth
Boilerplate / scaffolding	Fast	Surface check
UI components	Medium	Visual + logic
Business logic	Careful	Detailed review
Auth / security	Very careful	Security-focused
Data migrations	Very careful	Dry-run first
Payment handling	Very careful	Idempotency check

Effective Iteration Patterns

Pattern 1: The Narrowing Funnel

Start broad, narrow with each iteration.

Iteration 1: Get the shape

Create a data fetching hook for user profiles.

Iteration 2: Add error handling

Add loading states, error handling, and retry logic for transient failures.

Iteration 3: Integrate with existing tools

Refactor to use our React Query setup with our custom queryClient.

Iteration 4: Type and polish

Add TypeScript types for the response:
{ id: string, name: string, email: string, role: 'admin' | 'user' }

Pattern 2: Spike and Refine

Prototype first, harden second.

Spike (get it working):

Create a quick CSV parser. Don't worry about edge cases.
Just get basic parsing working for my test file.

Refine (make it production-ready):

This works for my test file. Now harden it:
- Handle quoted fields with commas inside
- Handle newlines within quotes
- Stream large files instead of loading all into memory
- Add proper error messages for malformed input
- Add TypeScript types

Pattern 3: The Comparison Loop

When unsure about approach, generate options.

Show me three different ways to implement debouncing in TypeScript.
Explain trade-offs of each.

Then:

Let's go with option 2. Expand it to support:
- Leading edge execution
- Maximum wait time
- A cancel method

Pattern 4: The Rubber Duck

When stuck, explain the problem.

I'm implementing cursor-based pagination. Requirements:
- Forward and backward navigation
- Stable results even if data changes mid-pagination

I'm confused about backward navigation. Here's my current code:

[paste code]

What am I missing?

AI’s response often clarifies your thinking, even if you don’t use all suggestions.

Complete Feature Example

Let’s build a data export feature through the vibe loop.

Context

Next.js app, need to export user data as CSV. Admin-only feature.

---

Iteration 1: Basic Structure

Prompt:

Create an API route at /api/users/export that queries users from
Prisma and returns a CSV download. Next.js 14 App Router.

AI Output: Basic route that works

Review:

• ✅ Route structure correct
• ✅ Basic CSV generation works
• ❌ No auth check—anyone can download
• ❌ No pagination—will crash on large datasets
• ❌ Includes sensitive fields (password hash)

---

Iteration 2: Security and Performance

Prompt:

Good start. Fix these issues:
1. Add auth check—only admins can export
2. Only include: id, email, name, createdAt
3. Stream the response for large datasets

AI Output: Adds auth, filtering, streaming

Review:

• ✅ Auth check added
• ✅ Fields filtered
• ✅ Streaming implemented
• ⚠️ No rate limiting
• ⚠️ Filename should include date

---

Iteration 3: Polish

Prompt:

Almost there. Final touches:
1. Rate limit: max 10 exports/hour per user
2. Filename: users-export-2026-01-25.csv
3. Add audit logging
4. Add 30-second timeout

AI Output: Production-ready code

Review:

• ✅ All requirements met
• ✅ Code is clean and readable
• ✅ Error handling solid
• ✅ Ready to ship!

---

Total: 3 iterations. Each one caught issues early and built on the last.

When to Exit the Loop

You’re done when:

Criterion	Check
Requirements met	All specified functionality works
Readable	Another dev could understand it
Edge cases handled	Errors don’t crash the system
Tests pass	Automated and manual
You’re confident	You’d ship this

Common Mistakes

❌ The One-Shot Fallacy

Mistake: Expecting perfect code from one prompt. Fix: Expect 2-5 iterations. That’s the process, not a failure.

❌ The Yes-Man Trap

Mistake: Accepting all AI output without review. Fix: You’re the quality gate. Always question.

❌ The Infinite Loop

Mistake: Endless refinement chasing perfection. Fix: Define “done” upfront. Ship when you hit it.

❌ The Context Dump

Mistake: Pasting entire files when a snippet would do. Fix: Start with relevant context. Add more only if AI seems confused.

❌ The Assumption Spiral

Mistake: Assuming AI knows your patterns.

Use our standard logger  ← AI doesn't know what this is

Fix: Show, don’t assume:

Use our logger pattern:
import { logger } from '@/lib/logger';
logger.info('message', { metadata });

Practice Exercise

Try this to internalize the loop:

1. Pick a small feature (15-30 min of work)
2. Write your first prompt (be specific but bounded)
3. Review with the checklist (use the review checklist above)
4. Refine based on review (be specific about changes)
5. Repeat until done (track how many iterations)
6. Reflect: What did you learn? What would you prompt differently?

What’s Next

The vibe loop is your foundation. But code doesn’t always work on the first try.

Debugging with AI →

Learn how to leverage AI when things go wrong—because they will.

Key Takeaways

• Prompt → Generate → Review → Refine is the core loop
• 2-5 iterations per feature is normal and expected
• You’re the quality gate — review everything critically
• Vary loop speed — fast for boilerplate, careful for security
• Define “done” upfront — then ship when you hit it
• Small iterations beat big ones — narrow focus, fast feedback

The vibe loop is simple to understand but takes practice to master.

---

🎯 Practice Right Now

1. Open your AI tool
2. Pick a simple project
3. Run the loop 3 times consciously:
   • Loop 1: Get something working
   • Loop 2: Fix the obvious issues
   • Loop 3: Polish and ship

That’s it. You just vibecoded. 🔄

---

Quick Iteration Prompts

# After reviewing output
"Good start. Make these changes:
1. [specific change]
2. [specific change]
Keep everything else the same."

# When it's close but not right
"Almost perfect. The only issue is [problem]. Fix just that."

# When you need to pivot
"Different approach. Instead of [current], let's try [alternative]."

# When it's done
"This looks good. Add comments explaining [complex part]."

---

Related Resources

Core Guides:

• Debugging with AI — When iterations reveal bugs
• Testing AI Code — Validate each iteration
• Prompting Fundamentals — Better prompts, better iterations

Cheatsheets:

• The Vibe Loop — Quick reference
• Quick Wins — Iteration prompts
• AI Debugging Checklist — When things break

Practice:

• 10 First Project Ideas — Practice the loop
• Build an AI App in 30 Minutes — See iteration in action