Vibe coding — describing what you want in natural language and letting AI generate the code — has produced some impressive demos. Full-stack apps from a single prompt. Working UIs from screenshots. Functioning APIs from descriptions.
It has also produced some spectacular failures in production.
What Vibe Coding Gets Right
For prototyping, vibe coding is genuinely revolutionary:
- Validate an idea in hours instead of weeks
- Generate UI layouts faster than any designer
- Create working demos for investor pitches
- Explore technical approaches without writing boilerplate
If your goal is "does this concept work?", vibe coding delivers.
Where It Falls Apart
1. No Architecture
AI generates code that works. It doesn't generate code that's maintainable, scalable, or consistent with your existing patterns. Every vibe-coded feature is an island — it works in isolation but doesn't integrate with anything.
2. No Error Handling
Happy paths only. The generated checkout flow works when the user has a valid card, stable internet, and follows the expected sequence. It crashes when the payment provider times out, the user double-clicks submit, or the session expires mid-checkout.
3. No Knowledge of Your System
AI doesn't know your database schema, your authentication model, your deployment pipeline, or your team's conventions. The generated code will use different patterns than your existing codebase, creating maintenance debt from day one.
4. Hidden Dependencies
Vibe-coded features often pull in libraries you don't need, duplicate functionality that already exists in your codebase, or create data models that conflict with your existing schema.
The Handoff Problem
The real cost of vibe coding surfaces when it's time to maintain, extend, or debug the generated code. The engineer assigned to "fix the checkout flow" now has to:
- Understand code they didn't write (and that follows no established patterns)
- Identify which parts are generated boilerplate vs. actual business logic
- Figure out which dependencies were intentional vs. accidental
- Integrate the code with existing systems that the AI knew nothing about
This is the Understanding Tax at its worst — applied to code that was designed to be generated quickly, not understood later.
The Right Approach
Vibe coding for prototyping. Engineering for production. And pre-code intelligence to bridge the gap:
- Prototype with Lovable, v0, or Cursor Composer
- Analyze the generated code against your actual codebase: what conflicts? What duplicates? What's missing?
- Plan the production implementation: map the prototype's features to your existing architecture
- Build the production version using your established patterns, with the prototype as a reference
The AI helps you build fast. Codebase intelligence helps you build right.
Originally published on glue.tools. Glue is the pre-code intelligence platform — paste a ticket, get a battle plan.
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