The Vibe Coding Problem: Why 'Just Prompt It' Doesn't Scale (And What Does)

TL;DR: "Vibe coding"—casually prompting AI without specs—works for demos and prototypes. It fails catastrophically as projects grow because AI lacks persistent context. The fix: spec-driven development.

Table of Contents

1. What is Vibe Coding?
2. Why Vibe Coding Is So Appealing
3. The Scaling Problem
4. Real Failure Patterns
5. The Math of Vibe Coding
6. When Vibe Coding Works
7. When Vibe Coding Breaks
8. The Spec-Driven Alternative
9. Transitioning from Vibe to Spec
10. FAQs

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What is Vibe Coding?

Vibe coding (popularized by Google AI Studio's "vibe mode" and developer culture) is the practice of:

1. Having an idea
2. Prompting an AI tool with minimal context
3. Accepting and pasting output
4. Fixing errors as they appear
5. Repeating until it works

The Vibe Coding Workflow

Idea → Prompt → Paste → Error → Fix → Prompt → Paste...

Characteristics:

• No formal specifications
• Minimal upfront planning
• High prompt-to-code ratio
• "Just make it work" mentality
• Context provided per-prompt, not persistently

Where the Term Comes From

Andrej Karpathy (Tesla AI, OpenAI) and others have described this approach as "vibing with the model"—a creative, exploratory style of development where you:

"Give in to the vibes, embrace exponentials, and forget that the code even exists."

Google's AI Studio explicitly promotes "vibe coding" as a feature for rapid prototyping.

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Why Vibe Coding Is So Appealing

The Demo Effect

Vibe coding produces impressive demos fast:

• "I built a landing page in 5 minutes!"
• "I created a full CRUD app in an hour!"
• "Look at this dashboard I made by just describing it!"

These results are real and exciting. They sell the vision of AI-first development.

The Friction Reduction

Traditional development has friction:

• Requirements gathering
• Architecture design
• Documentation
• Code review processes

Vibe coding eliminates all of this. Just prompt and go.

The Learning Curve

Vibe coding has nearly zero learning curve:

• No new methodologies to learn
• No templates to follow
• No documentation standards
• Just describe what you want

The Dopamine Loop

Vibe coding creates a powerful feedback loop:

1. Prompt
2. See immediate result
3. Feel productive
4. Repeat

This loop is addictive—and misleading about actual productivity.

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The Scaling Problem

What Changes as Projects Grow

Factor	Small Project	Large Project
Files	5-20	100+
Components	3-10	50+
API endpoints	2-5	30+
Database tables	1-5	20+
Team members	1	2-10+
Dependencies	Few	Many
Context needed	Can hold in head	Impossible to remember

The Context Window Limit

AI tools have finite context windows:

• GPT-4: 128K tokens
• Claude: 200K tokens
• Even with large windows, your project exceeds them

As your codebase grows, AI can see less of it proportionally, leading to more hallucinations.

The Compounding Problem

Each vibe-coded feature adds:

• Inconsistent patterns
• Undocumented decisions
• Conflicting conventions
• Hidden dependencies

These compound. By feature #20, the codebase is a minefield of inconsistencies that even YOU can't navigate—let alone an AI.

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Real Failure Patterns

Pattern 1: The Contradiction Cascade

Week 1: Vibe code a user auth system using one pattern Week 2: Vibe code more features, AI uses different auth pattern Week 3: Features conflict, auth breaks in production Week 4: Debugging nightmare—which pattern is "correct"?

Pattern 2: The API Zoo

// File 1: Vibe coded in January
await fetch('/api/users');

// File 2: Vibe coded in February
await apiClient.get('/api/user');

// File 3: Vibe coded in March
await fetchApi('users', 'GET');

// File 4: Vibe coded in April
await usersService.getAll();

Result: Four different patterns for the same operation. Maintenance nightmare.

Pattern 3: The Schema Drift

Database over time:

• user_id, userId, id_user, user (all referencing users)
• created_at, createdAt, created, date_created
• is_active, active, status, enabled

Each vibe coding session used whatever column names AI suggested. Now your schema is chaos.

Pattern 4: The Undocumented Feature

You vibe coded a complex feature. It works (mostly). Three months later:

• You need to modify it
• You can't remember how it works
• There's no documentation
• The original prompts are lost
• You're debugging your own mystery code

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The Math of Vibe Coding

Short-Term Economics

Vibe Coding Time = Prompt + Paste + Quick Debug
                 = 5-30 minutes per feature

This looks efficient.

Long-Term Economics

Real Cost = (Initial Vibe Time)
          + (Inconsistency Debug Time × Feature Count)
          + (Pattern Conflict Resolution)
          + (Refactoring Cost)
          + (Onboarding New Team Members)
          + (Production Bug Fixing)

The Break-Even Point

Project Size	Vibe Coding Total Time	Spec-Driven Total Time
5 features	2 hours	4 hours
10 features	6 hours	6 hours
20 features	20 hours	10 hours
50 features	100+ hours	20 hours

Break-even: Around 10 features, spec-driven development becomes faster.

The Hidden Tax

Every vibe-coded line carries a "confusion tax":

• Future-you will struggle to understand it
• AI will hallucinate against it
• Team members will duplicate it incorrectly
• Tests will miss edge cases

This tax compounds silently until the project becomes unmaintainable.

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When Vibe Coding Works

Legitimate Use Cases

1. Proof of concepts

   • Not intended for production
   • Will be rewritten if successful
   • Speed is the only priority

2. One-off scripts

   • Single-use utilities
   • No maintenance expected
   • No team involvement

3. Learning and exploration

   • Trying new technologies
   • Understanding concepts
   • Not building real products

4. Hackathons and demos

   • 24-48 hour time limit
   • Judged on concept, not code quality
   • Throwaway after event

5. Very small projects

   • 3-5 features maximum
   • Single developer
   • No long-term maintenance

The Rule of Thumb

Vibe code if:

• You'll throw it away within a week
• You're the only one who'll ever touch it
• It has fewer than 5 features
• It doesn't handle real user data

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When Vibe Coding Breaks

Red Flags

1. You've started saying "it's somewhere in the codebase"
2. Different files handle similar things differently
3. You're afraid to refactor because "it might break something"
4. New features take longer than early features
5. You spend more time debugging than building
6. Onboarding takes days instead of hours
7. Production bugs are increasing, not decreasing

The Point of No Return

Once a vibe-coded project reaches "critical mass" of inconsistency, options narrow:

Option	Cost	Outcome
Continue vibe coding	Very high	Eventual failure
Gradual refactoring	High	Slow recovery
Spec and rewrite	Medium	Clean restart
Abandon project	Write-off	Sometimes correct

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The Spec-Driven Alternative

What Spec-Driven Means

Instead of prompt-per-feature, create persistent specifications that:

1. Define your project's structure
2. Document your conventions
3. Specify your APIs and schemas
4. Guide AI with consistent context

The Spec-Driven Workflow

Brain Dump → Specs Generated → AI Uses Specs → Consistent Code

Key Documents

Document	Purpose	Prevents
PRD	Define scope	Feature creep
API Spec	Define endpoints	API inconsistency
Database Schema	Define columns	Schema drift
Architecture	Define patterns	Pattern mixing
Conventions	Define standards	Style chaos

Why This Scales

Week 1: Create specs (1-2 hours with Context Ark) Week 2-52: AI references specs for every feature Result: Consistent patterns across entire codebase

The upfront investment pays dividends on every subsequent feature.

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Transitioning from Vibe to Spec

Step 1: Freeze New Vibe Coding

Stop adding new vibe-coded features. This stops the bleeding.

Step 2: Document What Exists

Create specs that describe your CURRENT state:

• What endpoints exist?
• What tables exist?
• What patterns are used?
• What conventions have emerged (even if inconsistent)?

Step 3: Establish Standards

From the chaos, pick winners:

• "We'll use THIS API pattern going forward"
• "We'll use THIS naming convention from now on"
• Document these as the new standard.

Step 4: Spec Future Work

All new features get speced before coding:

• PRD for requirements
• API spec for endpoints
• Schema changes for database

Step 5: Gradual Refactoring

Over time, update old vibe-coded sections to match new specs. Don't try to fix everything at once.

Timeline Expectation

Phase	Duration
Document existing state	1-2 days
Establish standards	1 day
Set up spec workflow	1 day
First spec-driven features	Immediate
Full codebase consistency	3-6 months

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FAQs

Isn't spec writing slow?

With Context Ark: 30-60 minutes for comprehensive specs Without Context Ark: 2-4 hours

Either is faster than the debugging time saved.

What about Google's Vibe Code promotion?

Google AI Studio's vibe coding is designed for:

• Prototyping
• Learning
• Demos
• Small experiments

It's not promoted as a production development methodology.

Can I vibe code the MVP then add specs?

You can, but you'll likely need to refactor the MVP significantly. If you plan to iterate on the product, start with specs.

How do specs work with AI tools?

• Cursor: Add specs to .cursor/rules/
• Cline: Reference in .clinerules
• Context Ark: Export ready-to-use packs

What if I'm a solo developer?

Specs help solo developers too:

• Future-you is a team member
• Specs prevent self-inflicted confusion
• AI tools perform better with specs

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Conclusion

Vibe coding is appealing because it's fast and fun in the short term. It fails because:

1. AI lacks persistent context
2. Inconsistencies compound
3. The codebase becomes unmaintainable
4. Debugging eclipses development

Spec-driven development invests 1-2 hours upfront to:

1. Define persistent context
2. Ensure consistency
3. Scale sustainably
4. Let AI actually help

The choice isn't "vibe OR structure." It's "invest now OR pay later."

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Resources

Blog Posts

• Context Engineering for Vibe Coders
• Spec-Driven Development Guide
• How to Stop AI Hallucinations

Templates

• Free Spec Templates
• PRD Template for AI Apps

Free Tools

• PRD Generator — Generate a PRD with non-goals and acceptance criteria
• Spec Readiness Score — Check your context coverage before building
• Scope Lock Wizard — Split MVP from Post-MVP features
• Cursor Rules Generator — Create .cursorrules for your tech stack

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Scale your AI coding. Generate specs with Context Ark →

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Last updated: January 2026