Vibe Coding Bible: Rethinking Software Architecture for AI-Generated Code

For the last year I noticed something consistent while working with AI-generated code.

The more I used AI to write code, the more my systems started to break in a very specific way:

• small changes caused unexpected side effects
• refactoring became increasingly risky
• context grew until it became difficult to manage
• AI sometimes “helped” by breaking nearby modules
• fixing one thing often broke something else

At some point I stopped thinking of this as a prompt engineering issue.

It felt more like a fundamental mismatch in the programming model itself.

The core mismatch

Most software architecture today is built around one assumption:

humans are the primary authors of code

We expect that:

• humans can understand the system over time
• humans can safely refactor large dependency graphs
• humans can maintain mental models of complexity
• humans can coordinate changes across modules

This works reasonably well when humans are the main drivers of development.

But with LLMs involved, a different behavior emerges:

every interaction effectively starts from a partial or reconstructed context

Even if the full codebase exists, the model operates with limited visibility at any given moment.

As systems grow, this leads to predictable patterns:

• increasing coupling
• fragile refactoring
• hidden dependencies
• loss of global consistency

The idea behind Vibe Coding Bible

This led me to a different question:

What if we design software assuming AI is the primary code author, and humans are system designers?

That shift changes many assumptions.

Some familiar practices start to behave differently:

• flexible interfaces become harder to reason about
• deep abstraction layers introduce fragility
• refactoring increases cognitive cost
• shared mutable structures amplify unexpected behavior

Core principle: nailed interfaces

The central idea is simple:

interfaces should not evolve

Once defined:

• they are never modified
• they are never extended
• they are never refactored

If requirements change, we do not modify the interface.

We create a new block with a new interface.

Even if it feels redundant.

Because AI makes duplication cheap — but coupling remains expensive.

Block structure

Every unit of code is a block:

• interface.py → immutable contract
• implementation.py → AI-generated logic
• tests.py → optional generated tests

Key rule:

the interface is frozen forever

No exceptions.

Tree instead of graph

Most real-world systems naturally evolve into dependency graphs:

• modules depend on each other
• dependencies spread in multiple directions
• small changes produce system-wide effects

In AI-heavy development, this becomes especially unstable.

So instead we enforce a simpler structure:

• strict tree hierarchy
• level-based imports only
• no cross-branch dependencies
• no lateral coupling

This is not elegant.

But it is predictable.

And predictability matters more than flexibility when AI writes most of the code.

No refactoring rule

In this model:

If something is wrong:

• we do not refactor it
• we replace it

If behavior changes:

• we do not extend existing modules
• we create new ones

If an interface no longer fits:

• we do not modify it
• we define a new block

At first this feels inefficient.

But the cost model shifts:

• writing new code is cheap
• managing coupling is expensive

So we optimize for regeneration instead of evolution.

What changes for developers

In this model, developers stop being:

• refactorers
• dependency graph managers
• large-codebase navigators

And become:

• system decomposers
• interface designers
• constraint engineers
• AI operators

The job shifts from writing code to shaping boundaries.

Migration strategy

Existing systems are not migrated directly.

Instead:

• freeze problematic modules
• stop modifying legacy code
• build new AI-native blocks alongside it
• gradually shift functionality over time

Legacy code becomes background context, not something to constantly fix.

This is not a universal rule

This is an experiment.

It may not apply to all systems.

Some domains may still require:

• complex shared state
• deep optimization
• tightly coupled systems

But in many AI-heavy workflows, traditional assumptions start to break down.

Why I’m writing this

I call this experiment:

Vibe Coding Bible

It is not a finished framework.

It is a set of assumptions I am testing in practice.

Related projects

• Programming paradigm for AI-written software:
  
  https://github.com/evgeniykormin86-stack/Programming-Paradigm-for-AI-Written-Software

• Incident-driven AI system (Golden Armada):
  
  https://github.com/evgeniykormin86-stack/golden_armada

Final thought

The question I’m exploring is simple:

What does software architecture look like when AI becomes the primary programmer?

This is one possible answer.

Not the final one.

But a starting point.