Vibe Coding vs. Spec Coding: How Kiro is changing the way we build AI-Powered Software

Artificial Intelligence has fundamentally changed how we build software.

Today, it's possible to generate an entire REST API, a React dashboard, or even a complete SaaS application simply by chatting with an AI assistant. A few prompts later, you have authentication, a database, tests, and deployment scripts in place.

This workflow has become known as Vibe Coding*.
It's fast, it's fun, and honestly, it's incredibly impressive.

But after spending months building production systems with AI, I've noticed something interesting: the challenge is no longer generating code. The challenge is keeping the project coherent as it grows.

This is exactly where Spec Coding, popularized by AWS Kiro, comes into play. Instead of treating AI as a code generator, it treats AI as a software engineer, one that first understands what needs to be built before deciding how to build it.

Let's explore why this shift matters.

What Is Vibe Coding?

Vibe coding is an informal development style where software is built almost entirely through conversations with AI. The workflow usually looks something like this:

"Create a REST API for user management."

The AI generates the project. Then:

"Add JWT authentication."

A few seconds later:

"Use PostgreSQL instead of SQLite."

Then:

"Generate a React admin dashboard."

And so on. The project evolves naturally through prompts, you're essentially brainstorming with an AI that happens to write code.
For prototypes, it's almost magical.

Why Everyone Loves It

There's a good reason vibe coding became so popular: it dramatically reduces the time between an idea and a working application.

Instead of spending hours setting up project structures, dependency injection, authentication, Docker, CI/CD, or infrastructure, you simply ask, and within minutes you have something tangible.

This makes vibe coding particularly effective for:

• MVPs
• Learning new frameworks
• Proof of Concepts
• Internal tools
• Personal projects

The feedback loop is incredibly short: think, prompt, test, improve. For experimentation, it's hard to beat.

Your Architecture Lives Inside a Chat

Imagine joining a project six months later.
Where is the architecture documented ? Why was DynamoDB chosen over the alternatives? Why are events published asynchronously ? Why does one service use CQRS while another doesn't ?

With vibe coding, the answers often exist only inside previous conversations. If those chats disappear, so does the reasoning behind the software.

Prompt Engineering Becomes Project Management

As the application grows, prompts become increasingly complex.

Instead of asking:

Add OAuth authentication.

You start writing prompts like:

Add OAuth authentication while preserving the existing layered architecture, respecting our repository pattern, using the current DTO conventions, keeping backward compatibility with the existing APIs, updating the Terraform modules, generating tests, and documenting the changes.

Eventually, you're no longer describing the feature, you're reconstructing the project's entire context every single time.

Enter Spec Coding

Spec Coding takes a completely different approach.
Instead of generating code immediately, it starts by generating knowledge.

The AI first defines:

• What needs to be built
• Why it needs to exist
• How it should be designed
• How it will be implemented

Only then does it start writing code.
This may sound slower, but it often makes large projects move much faster in the long run.

Kiro's Philosophy

Kiro is built around Specification-Driven Development.
Rather than relying solely on conversation history, it stores project knowledge as version-controlled artifacts.

Instead of asking the AI to "remember" everything, you provide it with structured documentation that evolves alongside the codebase.
The specification becomes the project's single source of truth.

A Practical Example

Suppose we want to build an AWS Dead Letter Queue (DLQ) Redrive application.

With traditional vibe coding, we might simply write:

Build a dashboard that allows users to inspect and redrive messages from SQS Dead Letter Queues.

And the AI would immediately start coding.

With Kiro, the workflow looks very different.

Step 1 — Requirements

Before generating any code, Kiro defines the functional requirements.

For example:

Functional Requirements

• View available DLQs
• Inspect failed messages
• Search messages
• Redrive selected messages
• Redrive all messages
• Display CloudWatch metrics
• Show processing history

Non-functional Requirements

• IAM authentication
• Audit logging
• Multi-account support
• Responsive UI
• High availability

Everything is explicit. Nothing is hidden inside prompts.

Step 2 — Design

Next comes the architecture. Instead of inventing it during implementation, Kiro proposes a technical design upfront:

Frontend
├── React
├── Material UI
└── React Query

Backend
├── FastAPI
├── Boto3
└── Pydantic

AWS
├── SQS
├── CloudWatch
├── IAM
└── CloudTrail

Infrastructure
└── Terraform

Now every future implementation shares the same architectural vision.

Step 3 — Tasks

Once the specification is validated, implementation gets decomposed into manageable tasks, such as:

• Create REST API
• Implement SQS service
• Create CloudWatch integration
• Build React dashboard
• Implement authentication
• Write unit tests
• Generate documentation

Each task references one or more requirements, so traceability becomes automatic.

Step 4 — Code Generation

Only now does Kiro generate code.
The difference is subtle but important: the AI isn't improvising anymore, it's implementing an approved design.

Onboarding Becomes Easier

Imagine a new developer joining your project. Instead of digging through Slack messages and old AI conversations, they can simply open the requirements, the architecture, the tasks, and the decisions behind them. In less than an hour, they understand the project. That's invaluable.

Documentation Stops Being an Afterthought

One of the biggest benefits is that documentation is no longer something you write at the end, it's created before implementation even begins. As a result, requirements stay synchronized, architecture stays documented, and implementation follows the specification. Documentation becomes part of development, not a separate activity bolted on afterward.

Comparing Both Approaches

My Recommended Workflow

Instead of choosing one approach over the other, combine them.

Phase 1 — Explore. Use vibe coding to rapidly answer questions like: Is the idea viable? Which technology should we use ? Can we build this ? What are the trade-offs ? Speed matters here.

Phase 2 — Formalize. Once the concept proves valuable, write requirements, define architecture, identify constraints, and create implementation tasks. This becomes your project's foundation.

Phase 3 — Build. Now let AI generate code from the specification instead of from memory. You'll get code that's more consistent, easier to review, easier to maintain, and easier to extend.

Final Thoughts

AI has made software development dramatically faster, but software engineering has never been only about writing code. It's about understanding requirements, making architectural decisions, collaborating with teammates, documenting intent, and maintaining systems for years.

It's vibe coding for exploration, and spec coding for engineering. AI shouldn't only help us write code faster, it should help us build better software, and that's exactly where tools like Kiro are pushing the industry forward.

Photo by Yaroslav Muzychenko on Unsplash