The Evolution of AI Coding: From Vibe Coding to Spec-Driven Development

TL;DR: AI coding evolved from casual “vibe coding” through optimised assistants to spec-driven development. The frontier isn’t better code generation - it’s better specifications. And that only works if you have your ducks in a row. We help organisations get there.

The First Generation: Vibe Coding

Cursor, Cline, Roo Code and Kodu led the charge. They plugged into Visual Studio Code, connected to your choice of backend AI (GPT-4, Claude, Gemini) and let you chat your way through development.

“Make this button blue.” “Add error handling here.” “Refactor this to use async/await.”

It felt magical. For small changes and quick iterations, it was magical. But try building anything complex and the limitations became obvious fast.

• Vibe coding works brilliantly for:Quick modifications to existing code
• Exploratory prototyping where “good enough” is actually good enough
• Learning unfamiliar APIs or frameworks
• Generating boilerplate that you’ll immediately refactor

But it falls apart for anything requiring architectural thinking, consistency across multiple files or alignment with actual product requirements.

The Second Generation: Optimisation

Kilo, Gemini Code and Claude Code arrived as the second wave. They brought genuine improvements: better context handling, smarter multi-file editing, more sophisticated understanding of codebases.

They’re still fundamentally conversational tools. Still plugging into VS Code. Still letting you choose your backend AI. But they learned from first-generation limitations and delivered meaningful optimizations.

The problem? They’re still operating at the implementation level. You’re having better conversations about code, but you’re still having conversations about code rather than conversations about what you’re actually building.

The Third Generation: Spec-Driven Development

Now we’re seeing the real evolution: spec-driven development with tools like GitHub Spec Kit and Kiro.

Instead of chatting about implementation details, you start with specifications. Product requirements, design documents, system architecture - the actual engineering artefacts that determine whether you’re building the right thing.

Spec-driven tools align your product requirements, design decisions and coding tasks to a solid specification before generating a single line of code. When the specification is clear, the implementation becomes almost mechanical.

Microsoft’s Spec Kit emphasises this shift: start with the specification, get alignment across stakeholders, then let AI handle the implementation details.

Kiro takes this further, making specifications first-class citizens in the development process. The tool doesn’t just generate code from specs - it helps you write better specifications by understanding what makes them actionable.

Why This Actually Works

Here’s the insight driving spec-driven development: AI is brilliant at translation but terrible at invention.

Give AI a vague description and it invents plausible-sounding solutions that might not match your actual needs. Give AI a precise specification and it translates that specification into working code with impressive consistency.

The bottleneck was never AI’s ability to generate code. It was always our ability to clearly specify what we wanted built.

The Catch: You Need Your Ducks in a Row

Spec-driven development only works if your specifications are actually good.

Garbage specifications produce garbage code - just faster and more consistently than vibe coding would have. If you don’t understand your requirements, design and architecture well enough to specify them clearly, spec-driven tools won’t save you.

This is where most organisations struggle. They want AI to compensate for unclear requirements and inconsistent design thinking. It can’t. What it can do is execute clear specifications with remarkable efficiency.

Getting Your Ducks in a Row

During 18 months building Orange Octopus using AI coding tools, we learned these lessons the expensive way.  Inadequate specifications led to wrong implementations.  Ambiguous documentation created inconsistency.  Missing architectural decisions compounded into technical debt.

The breakthrough came from systematic governance: index.md navigation for clear structure, 500-line chunking for manageable context, Single Source of Truth for consistency.  Not sexy work, but it doubled the development velocity.

Most organisations using AI coding tools are hitting the same problems we have.  They need their documentation, specifications and governance frameworks sorted before spec-driven development can deliver value.

The Lanboss Perspective

At Lanboss AI, we help development teams get their ducks in a row. That means implementing the documentation architecture, specification management and governance frameworks that make spec-driven development actually work.

The tools are evolving fast - from vibe coding through optimisation to spec-driven development. But tools alone don’t solve the problem. You need the foundational governance that lets AI tools deliver their promised value.

Through our AI Coding Governance Implementation service, we help teams: