{"slug": "vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered", "title": "Vibe Coding vs. Spec Coding: How Kiro is changing the way we build AI-Powered Software", "summary": "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.", "body_md": "Artificial Intelligence has fundamentally changed how we build software.\n\nToday, 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.\n\nThis workflow has become known as *Vibe Coding**.\n\nIt's fast, it's fun, and honestly, it's incredibly impressive.\n\nBut 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.\n\nThis 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.\n\nLet's explore why this shift matters.\n\n**Vibe coding** is an informal development style where software is built almost entirely through conversations with AI. The workflow usually looks something like this:\n\n\"Create a REST API for user management.\"\n\nThe AI generates the project. Then:\n\n\"Add JWT authentication.\"\n\nA few seconds later:\n\n\"Use PostgreSQL instead of SQLite.\"\n\nThen:\n\n\"Generate a React admin dashboard.\"\n\nAnd so on. The project evolves naturally through prompts, you're essentially brainstorming with an AI that happens to write code.\n\nFor prototypes, it's almost magical.\n\nThere's a good reason **vibe coding** became so popular: it dramatically reduces the time between an idea and a working application.\n\nInstead 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.\n\nThis makes vibe coding particularly effective for:\n\nThe feedback loop is incredibly short: think, prompt, test, improve. For experimentation, it's hard to beat.\n\nImagine joining a project six months later.\n\nWhere 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 ?\n\nWith vibe coding, the answers often exist only inside previous conversations. If those chats disappear, so does the reasoning behind the software.\n\nAs the application grows, prompts become increasingly complex.\n\nInstead of asking:\n\nAdd OAuth authentication.\n\nYou start writing prompts like:\n\nAdd 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.\n\nEventually, you're no longer describing the feature, you're reconstructing the project's entire context every single time.\n\n**Spec Coding** takes a completely different approach.\n\nInstead of generating code immediately, it starts by generating **knowledge**.\n\nThe AI first defines:\n\nOnly then does it start writing code.\n\nThis may sound slower, but it often makes large projects move much faster in the long run.\n\nKiro is built around **Specification-Driven Development**.\n\nRather than relying solely on conversation history, it stores project knowledge as version-controlled artifacts.\n\nInstead of asking the AI to \"remember\" everything, you provide it with structured documentation that evolves alongside the codebase.\n\nThe specification becomes the project's single source of truth.\n\nSuppose we want to build an AWS Dead Letter Queue (DLQ) Redrive application.\n\nWith traditional vibe coding, we might simply write:\n\nBuild a dashboard that allows users to inspect and redrive messages from SQS Dead Letter Queues.\n\nAnd the AI would immediately start coding.\n\nWith Kiro, the workflow looks very different.\n\nBefore generating any code, Kiro defines the functional requirements.\n\nFor example:\n\n**Functional Requirements**\n\n**Non-functional Requirements**\n\nEverything is explicit. Nothing is hidden inside prompts.\n\nNext comes the architecture. Instead of inventing it during implementation, Kiro proposes a technical design upfront:\n\n```\nFrontend\n├── React\n├── Material UI\n└── React Query\n\nBackend\n├── FastAPI\n├── Boto3\n└── Pydantic\n\nAWS\n├── SQS\n├── CloudWatch\n├── IAM\n└── CloudTrail\n\nInfrastructure\n└── Terraform\n```\n\nNow every future implementation shares the same architectural vision.\n\nOnce the specification is validated, implementation gets decomposed into manageable tasks, such as:\n\nEach task references one or more requirements, so traceability becomes automatic.\n\nOnly now does Kiro generate code.\n\nThe difference is subtle but important: the AI isn't improvising anymore, it's implementing an approved design.\n\nImagine 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.\n\nOne 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.\n\n| Vibe Coding | Spec Coding |\n|---|---|\n| Conversation-driven | Specification-driven |\n| Immediate code generation | Specification before implementation |\n| Great for prototypes | Great for production systems |\n| Context stored in chats | Context stored in version-controlled artifacts |\n| Architecture emerges over time | Architecture defined upfront |\n| Fast initial development | Better long-term scalability |\n| Difficult onboarding | Easier collaboration |\n| Documentation often missing | Documentation generated first |\n\nInstead of choosing one approach over the other, combine them.\n\n**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.\n\n**Phase 2 — Formalize.** Once the concept proves valuable, write requirements, define architecture, identify constraints, and create implementation tasks. This becomes your project's foundation.\n\n**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.\n\nAI 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.\n\nIt'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.\n\nPhoto by [Yaroslav Muzychenko](https://unsplash.com/@yaro_muzychenko?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/black-and-white-airplane-in-a-building-uDa9QHn7SrA?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)", "url": "https://wpnews.pro/news/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered", "canonical_source": "https://dev.to/aws-builders/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered-software-1950", "published_at": "2026-08-04 13:19:58+00:00", "updated_at": "2026-08-04 13:48:53.389539+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools", "ai-products"], "entities": ["AWS Kiro", "Kiro"], "alternates": {"html": "https://wpnews.pro/news/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered", "markdown": "https://wpnews.pro/news/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered.md", "text": "https://wpnews.pro/news/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered.txt", "jsonld": "https://wpnews.pro/news/vibe-coding-vs-spec-coding-how-kiro-is-changing-the-way-we-build-ai-powered.jsonld"}}