Vibe Coding Meets Spec-Driven Development: The Best of Both Worlds

A developer has combined "vibe coding" with spec-driven development (SDD) to create a hybrid workflow that balances rapid prototyping with codebase stability. The approach involves writing a lightweight, 15-30 minute specification before using AI coding tools, then feeding that spec as context for every AI prompt to prevent context drift and maintain consistent intent. This method allows developers to move fast on implementation while ensuring the AI generates code that aligns with a predefined contract rather than optimizing for the last message.

If you've been coding with AI assistants lately, you've probably experienced both extremes. On one side: vibe coding , you open a chat, describe what you want in plain English, and let the model run. It feels like magic. You ship fast, you stay in flow, and the dopamine hits are real. On the other side, there's a nagging feeling. The codebase starts to drift. A component does three things it shouldn't. A bug appears that you swear you didn't introduce. You ask the AI to fix it, and it breaks something else. The vibes turn chaotic. Enter Spec-Driven Development SDD , the idea that before you write or generate a single line of code, you write a specification: a clear, structured document that defines what you're building, why , and how it should behave . It's not a new concept, but it's having a renaissance in the age of AI coding. The good news? You don't have to pick one. In fact, combining them is arguably the most productive way to build software today. The term was popularized by Andrej Karpathy in early 2025: "There's a new kind of coding I call vibe coding, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists." It's characterized by: Vibe coding shines for prototyping , exploration , and solo projects where speed matters more than maintainability. It's a legitimate and powerful workflow, but it has a ceiling. SDD is the practice of writing a machine-readable and human-readable specification before implementation begins. A spec typically includes: In an AI-assisted workflow, the spec becomes the single source of truth you feed into every prompt. Instead of re-explaining context each time, you anchor the model to a document it can reference. You: "Build me a user authentication system" AI: generates 300 lines of code You: "Actually add role-based access control" AI: refactors half the codebase You: "Wait, why is the session logic in the component?" AI: "Good point, let me move it..." You: three hours later, untangling spaghetti Without a spec, the AI optimizes for your last message , not your actual goal. Every new prompt is a new negotiation with the model, and context drift is inevitable. Heavy specification processes can kill momentum. Writing a 10-page PRD before you know if an idea even works is a trap. You over-engineer requirements for problems you haven't validated. The spec becomes a bureaucratic artifact nobody reads. The sweet spot is a lightweight spec that guides rapid AI-assisted implementation. Here's how it works in practice: Before touching any AI coding tool, spend 15–30 minutes writing a short spec. It doesn't need to be formal, a markdown file works perfectly. Feature: User Authentication Goal Allow users to register, log in, and manage sessions securely. Scope - IN: Email/password registration, JWT sessions, logout - OUT: OAuth, 2FA next iteration Data Model User { id, email, passwordHash, createdAt, role: "user" | "admin" } Session { token, userId, expiresAt } API Contracts POST /auth/register → { token, user } POST /auth/login → { token, user } POST /auth/logout → { success: boolean } GET /auth/me → { user } Acceptance Criteria - Passwords hashed with bcrypt min 12 rounds - JWT expires in 7 days - Invalid credentials return 401, never expose which field failed - Logout invalidates the token server-side That's it. Two hundred words that prevent hours of confusion. Every AI prompt now starts with context from the spec: Given this spec: paste relevant section Implement the POST /auth/register endpoint. Use bcrypt for hashing and return a signed JWT. Follow the data model defined above. The model is no longer guessing your intent, it's executing against a contract. Once the structure is in place, let the vibes flow. Need to add error handling? Optimize a query? Style a component? You can move fast because the foundation is solid. The spec sets the walls; the vibe coding furnishes the room. This is the discipline that separates good hybrid workflows from bad ones. When scope changes and it will , update the spec first , then regenerate or refactor the code. Don't let the spec become stale documentation, it's a living contract. Step 1: Edit spec.md → add OAuth section Step 2: Prompt AI with updated spec section Step 3: Let it implement Let's say you want to build a simple task manager. Here's how the hybrid approach plays out: Pure vibe approach: "Build a task manager with React and Node" You'll get something , but what database? What auth? What data model? You'll spend the next hour correcting assumptions. Hybrid approach: Write a 10-minute spec: Task Manager MVP Stack: React + Express + SQLite simple, no Docker needed Auth: None for MVP single user, local app Data Model Task { id, title, status: "todo"|"in progress"|"done", createdAt } UI Requirements - List view grouped by status - Add task via inline input - Drag or click to change status - No delete archive instead Out of Scope - Multi-user, tags, due dates, notifications v2 Now your AI prompts are laser-focused: "Using this spec, generate the Express API with SQLite. Only implement the endpoints needed for CRUD on tasks." The result? Clean, predictable code that matches what you actually want. The spec-then-vibe approach pairs beautifully with AI tools that support long context or file-based prompting: The key is making your spec accessible to the AI at all times, not just in the first prompt. | Situation | Recommended Mode | |---|---| | Exploring a new idea | Pure vibe, validate fast | | Building a feature in a production codebase | Spec first, always | | Solo weekend project | Light spec 30 min max | | Team collaboration | Full spec with acceptance criteria | | Debugging / fixing issues | Vibe with the existing spec as context | | Greenfield product | Spec-driven from day one | The biggest change isn't technical, it's philosophical. Vibe coding tempts you to treat the AI as an oracle: just ask and receive . Spec-driven development reminds you that you are still the architect . The AI is an extraordinary executor, but it needs direction. When you combine both, you get something powerful: the speed of vibe coding without the chaos, and the structure of specs without the bureaucracy. Think of it this way: specs are the map, vibe coding is the engine . You can drive fast, but you still need to know where you're going. You'll be surprised how much less time you spend fixing AI hallucinations, and how much more time you spend shipping things that actually work. Have you tried combining vibe coding with specs? What's your workflow? Drop a comment below, I'd love to hear how others are navigating this.