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

AWS Kiro is introducing 'Spec Coding,' a specification-driven development approach that treats AI as a software engineer rather than a code generator, aiming to maintain project coherence as AI-generated software grows. The method stores project knowledge as version-controlled artifacts, making the specification the single source of truth, in contrast to the informal 'Vibe Coding' style that relies on conversational prompts.

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. 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. 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: The feedback loop is incredibly short: think, prompt, test, improve. For experimentation, it's hard to beat. 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. 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. Spec Coding takes a completely different approach. Instead of generating code immediately, it starts by generating knowledge . The AI first defines: 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 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. 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. Before generating any code, Kiro defines the functional requirements. For example: Functional Requirements Non-functional Requirements Everything is explicit. Nothing is hidden inside prompts. 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. Once the specification is validated, implementation gets decomposed into manageable tasks, such as: Each task references one or more requirements, so traceability becomes automatic. Only now does Kiro generate code. The difference is subtle but important: the AI isn't improvising anymore, it's implementing an approved design. 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. 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. | Vibe Coding | Spec Coding | |---|---| | Conversation-driven | Specification-driven | | Immediate code generation | Specification before implementation | | Great for prototypes | Great for production systems | | Context stored in chats | Context stored in version-controlled artifacts | | Architecture emerges over time | Architecture defined upfront | | Fast initial development | Better long-term scalability | | Difficult onboarding | Easier collaboration | | Documentation often missing | Documentation generated first | 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. 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. 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