What Is Vibe Coding? A New Approach to AI-Assisted Development

Shunku
#AI#Vibe Coding#Development

Imagine writing software by simply describing what you want it to do—almost like talking to a teammate—while an AI translates your ideas into code. This is vibe coding, a term coined by AI pioneer Andrej Karpathy to describe a prompt-first, exploratory approach to programming where developers "fully give in to the vibes" of AI assistance.

The AI Coding Spectrum

AI-assisted development exists on a spectrum, from loose exploration to structured engineering:

flowchart LR
    A[Vibe Coding] --> B[AI-Assisted Coding] --> C[Traditional Development]

    A --> A1["Prompt-first<br/>Exploratory<br/>Rapid iteration"]
    B --> B1["Hybrid approach<br/>AI as partner<br/>Human oversight"]
    C --> C1["Manual coding<br/>Full control<br/>Deep understanding"]

    style A fill:#8b5cf6,color:#fff
    style B fill:#3b82f6,color:#fff
    style C fill:#10b981,color:#fff

Vibe Coding Approach

In vibe coding, you:

  • Describe functionality in natural language
  • Let AI generate initial implementation
  • Iterate through conversation
  • Accept "good enough" solutions
  • Prioritize speed over perfection

AI-Assisted Engineering Approach

In structured AI-assisted development:

  • Plan architecture before prompting
  • Review and understand all generated code
  • Maintain full codebase comprehension
  • Use AI as a productivity tool, not replacement
  • Prioritize maintainability

How Vibe Coding Works

flowchart TD
    A[Describe Intent] --> B[AI Generates Code]
    B --> C{Does it work?}
    C -->|No| D[Refine Description]
    D --> B
    C -->|Yes| E{Good enough?}
    E -->|No| F[Request Changes]
    F --> B
    E -->|Yes| G[Ship It]

    style A fill:#f59e0b,color:#fff
    style G fill:#10b981,color:#fff

Example Conversation

You: Create a React component that displays a list of users with
their names and emails. Add a search box to filter users.

AI: [Generates component code]

You: The search should be case-insensitive and also filter by email.

AI: [Updates the code]

You: Add loading and error states.

AI: [Adds loading spinner and error handling]

You: Perfect, that works!

When Vibe Coding Shines

Ideal Use Cases

Use Case Why It Works
Prototypes Speed over polish
One-off scripts Disposable code
Learning projects Exploration mode
Hackathons Time pressure
Internal tools Lower quality bar
Proof of concepts Validate ideas fast

Example: Quick Prototype

Prompt: Create a simple expense tracker app with:
- Add/delete expenses with amount and category
- Show total by category
- Display as a pie chart
- Use localStorage for persistence

Just make it work, don't worry about perfect code structure.

This prompt can generate a working prototype in minutes that would take hours to code manually.

Where Vibe Coding Struggles

Limitations

flowchart TD
    A[Vibe Coding Challenges]
    A --> B[Complex Architecture]
    A --> C[Performance Optimization]
    A --> D[Security-Critical Code]
    A --> E[Large Codebase Integration]
    A --> F[Debugging Subtle Issues]

    style A fill:#ef4444,color:#fff
Challenge Why It's Hard
Complex systems AI lacks full context
Performance tuning Requires deep analysis
Security AI may miss vulnerabilities
Legacy integration Needs existing code understanding
Debugging AI doesn't see runtime behavior

The 70% Problem

AI can often get you 70% of the way to a solution quickly, but the last 30% requires human expertise:

Initial AI output: 70% functional
+ Debugging: 80%
+ Edge cases: 90%
+ Production readiness: 100%

The last 30% often takes more time than the first 70%.

The Vibe Coder's Toolkit

Essential Skills

  1. Prompt clarity: Describe intent precisely
  2. Iteration speed: Quick feedback loops
  3. Code reading: Understand what AI generates
  4. Pattern recognition: Spot common AI mistakes
  5. Knowing when to stop: Accept good enough

Effective Prompting for Code

Good prompt structure:
1. Context: What you're building
2. Requirements: Specific functionality
3. Constraints: Technology, style, limitations
4. Examples: Input/output samples
5. Quality notes: What matters, what doesn't

Example:

Context: Building a REST API with Express.js
Requirements:
- POST /users endpoint to create users
- Validate email format and required fields
- Return appropriate HTTP status codes
Constraints:
- Use async/await
- No database, just in-memory array
Quality: Working code, don't need extensive error handling

Balancing Vibes and Engineering

The best approach often combines both mindsets:

flowchart LR
    subgraph Vibe Coding Phase
        A[Rapid prototyping]
        B[Feature exploration]
    end

    subgraph Engineering Phase
        C[Code review]
        D[Refactoring]
        E[Testing]
    end

    A --> C
    B --> C
    C --> D
    D --> E

    style A fill:#8b5cf6,color:#fff
    style B fill:#8b5cf6,color:#fff
    style C fill:#3b82f6,color:#fff
    style D fill:#3b82f6,color:#fff
    style E fill:#10b981,color:#fff

Workflow Recommendation

  1. Start with vibes: Get something working
  2. Validate the approach: Does the prototype prove the concept?
  3. Shift to engineering: Review, refactor, test
  4. Iterate: Use AI for specific improvements
  5. Own the code: Understand every line you ship

Summary

Aspect Vibe Coding Traditional
Speed Very fast Slower
Control Less Full
Understanding May be partial Complete
Best for Prototypes, learning Production systems
Risk Technical debt Over-engineering

Vibe coding is a powerful tool when used appropriately. Embrace it for rapid exploration and prototyping, but know when to shift into engineering mode for code that needs to be maintained, scaled, or secured.

The future likely involves fluid movement between these approaches—knowing when to let the vibes flow and when to take back control.

References

  • Osmani, Addy. Beyond Vibe Coding. O'Reilly Media, 2025.
  • Karpathy, Andrej. "There's a new kind of coding emerging..." Twitter/X, 2024.
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