Vibe Coding – Orchestrating AI Systems to Build Real Software

Vibe coding is a term I use for a deliberate, structured way of building software with multiple AI systems, where each tool has a clearly defined role.
It is not about letting an AI “just build an app”. Instead, it is about augmenting an experienced developer’s workflow.

In this post, I’ll explain how I use:

• ChatGPT as a planner, architect, and coordinator
• GitHub Copilot as a fast, focused programmer

This approach works surprisingly well — if you already understand software architecture, trade-offs, and long-term maintenance. Without that foundation, vibe coding quickly turns into chaos.

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🧩 What Vibe Coding Actually Means

At its core, vibe coding separates responsibilities:

• One AI focuses on thinking
• Another AI focuses on typing

This mirrors how experienced teams work:

• Architects design systems
• Developers implement them

Role Separation

Role	AI System	Responsibility
Planner	ChatGPT	Architecture, scope, constraints, iteration planning
Implementer	GitHub Copilot	Code generation inside a well-defined context

The human stays responsible for:

• Final decisions
• Code reviews
• Correctness
• Product quality

AI assists — it does not replace judgment.

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🧠 ChatGPT as the Planner and Coordinator

ChatGPT shines when used outside the code editor.

Typical tasks I delegate to it:

• Defining application goals
• Creating feature breakdowns
• Designing data models
• Proposing folder and module structures
• Writing implementation instructions for Copilot
• Identifying edge cases and risks

Example Planning Output

Phase 1:
- Define domain entities and responsibilities
- Create data model with clear ownership
- Decide on state management strategy

Phase 2:
- Implement core workflows
- Add persistence layer
- Introduce error handling and validation

Phase 3:
- Notifications, scheduling, and retries
- UX refinements
- Hardening and edge-case handling

This kind of output is ideal input for the next step.

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💻 GitHub Copilot as the Programmer

Copilot works best when:

• The scope is small
• The context is clear
• The instructions are explicit

I never ask Copilot to “build an app”.
I ask it to:

• Implement one feature
• Inside one folder
• Following specific constraints

Example Instruction Style

Implement the repository layer for the existing domain model.
- Use immutable data classes
- No UI logic
- Proper error handling
- Unit-testable design

Copilot then does what it does best:

• Boilerplate
• Consistent patterns
• Fast iteration

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🏗️ Architecture Is the Make-or-Break Factor

This approach only works if the overall architecture is solid.

Key architectural decisions must be made before heavy AI usage:

• Layered vs feature-based structure
• State ownership and lifecycle
• Persistence strategy
• Error propagation
• Extensibility points

AI cannot discover good architecture — it can only execute it.

If you don’t define boundaries, Copilot will happily blur them.

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🔄 Iterative Development with AI in the Loop

Vibe coding works best in short, controlled loops:

1. Define a small milestone 🧭
2. Ask ChatGPT to refine it
3. Feed precise instructions to Copilot
4. Review and refactor manually
5. Repeat

Why Iteration Matters

AI-generated code:

• Often works
• Sometimes surprises
• Occasionally breaks invariants

Frequent checkpoints prevent architectural drift.

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🧨 Common Pitfalls and Failure Modes

Treating AI as a Junior Developer

AI does not:

• Understand business intent
• Anticipate long-term maintenance
• Feel pain when things break later

If you wouldn’t give a task to a junior developer without guidance, don’t give it to AI.

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No Clear Ownership of Decisions

When everything is “AI-generated”, nobody feels responsible.

You must actively decide:

• What stays
• What changes
• What gets deleted

Deleting AI-generated code is a skill — use it.

Over-Engineering Too Early

AI loves patterns.
Sometimes too much.

Avoid:

• Premature abstractions
• Unnecessary layers
• “Future-proofing” without a real use case

Simple first, scalable later.

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🧑‍💻 Why This Does NOT Work for Beginners

This is the uncomfortable truth:

Without solid knowledge of software architecture, this approach almost always fails.

Reasons:

• You cannot detect bad design
• You cannot evaluate trade-offs
• You cannot spot subtle bugs
• You don’t know what to ask

AI amplifies your skill level — it does not create it.

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⚙️ What You Still Need to Do Yourself

Even with perfect AI usage, you must handle:

• Code reviews
• Refactoring
• Testing strategy
• Performance considerations
• Security and data integrity

AI helps you move faster — not skip fundamentals.

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🧠 Final Thoughts

Vibe coding is not magic. It is leveraging multiple AI systems with intent and discipline.

Used correctly, it:

• Speeds up development
• Reduces mental overhead
• Improves consistency

Used incorrectly, it:

• Produces fragile software
• Hides architectural flaws
• Creates long-term maintenance nightmares

If you already know how to build software, vibe coding feels like a force multiplier.
If you don’t — it feels like progress until it suddenly doesn’t.

And that difference matters.

Build with a vibe — but keep your hands on the wheel.

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