Spec-Driven Development: Keeping the Vibe, Adding a Plan

You’re sitting with an AI pair programmer (Claude Code, Cursor, Zencoder, etc.), sketching out a prototype. “Make it do this,” you say. Code materializes. You tweak a line. Request another feature. More code appears.

This is vibe coding. Pure instinct. Zero planning.

Developers abandon formal specifications, architectural blueprints, and methodical design. They trust gut feelings and live iteration. The process seduces with its spontaneity: software by impulse rather than intention. Short-term? Vibe coding delivers euphoric productivity. Code streams onto your screen at thought-speed. Creative momentum builds. The future feels endless.

Long-term? Different story entirely.

My first serious application started as a vibe-coded weekend project. Those initial sessions felt transcendent. The AI understood my half-formed ideas, filled gaps I hadn’t noticed, suggested improvements I wouldn’t have considered.

Then the codebase expanded. Features accumulated. Duplication increased. Errors piled up. My app became a tangled mess of spaghetti code.

The magic evaporated.

The Joy of “Vibe Coding” (and Where It Breaks)

Vibe coding feels like having a tireless programming partner. Throw an idea at the AI. Get code back in seconds. Need a UI component? Boilerplate appears instantly. Hit an error? Paste it, receive a fix.

No lengthy design documents. No upfront planning. Just code and improvise.

This spontaneity attracts indie hackers and hackathon participants. Early prototyping thrives on this approach. The “vibe” captures momentum and creative flow, perfect for quick experiments.

But the vibe only carries you so far.

Freeform development shows its limitations beyond toy projects. Without specifications or design, no strong guiding structure exists. The AI lacks full context about your application. It only knows what you’ve shown it in recent prompts.

Complex features require spoon-feeding guidance into each prompt. As my app grew, I found myself reminding the AI about earlier decisions. “No, we decided on a REST API, not GraphQL. Remember?”

Repeated misinterpretations multiplied. Vibe coding demands constant prompt-by-prompt guidance for non-trivial tasks or larger codebases. The AI has short memory: without an overarching plan, it veers off course or contradicts itself when projects get complex.

The Maintainability Problem

Maintainability becomes a nightmare. Nothing gets written down. Decision tracking becomes impossible. Why did we choose library X? Which authentication approach did we settle on?

Normal development processes capture these decisions in design documents or commit messages. In vibe coding, they scatter across chat logs. Not exactly team-friendly documentation.

If I handed this AI-generated code to a teammate, I’d need to dump massive unwritten context on them. Common issue: when you implement tasks via pure prompting, tracking decisions becomes impossible. The result? Spaghetti code with invisible rationale.

The “wing it” approach doesn’t scale. Vibe-coded projects often lack consistent design patterns, exhibit unpredictable behavior under real-world loads, and accrue serious technical debt.

I’ve witnessed this firsthand. The prototype that ran fine with one user started collapsing under pressure. Database queries slowed to a crawl. Features broke when I integrated new ones. General fragility emerged.

"The vibe felt good. The reality didn't scale."

In their experience helping startups, many founders built something in a weekend via vibe coding only to discover it couldn’t be extended, wasn’t secure, or behaved unpredictably in production.

I ended up spending as much time debugging and rewriting AI-written sections as I would have writing them from scratch. Not exactly the hyper-productivity I was promised.

Vibe coding alone wasn’t enough for anything beyond a toy app. I needed a way to keep that rapid, interactive development style without courting disaster when scaling up or handing off the project.

This is where I discovered spec-driven development.

What Is Spec-Driven Development?

Spec-driven development brings old-school planning and design into the AI coding loop without losing the fun part. The core idea is simple: before diving into code, you and the AI collaboratively produce a “spec” (specification) for what you’re building.

This spec can take the form of a brief design document, a list of requirements and tasks, or even user stories. The key is that it defines the software’s structure and behavior upfront, before any code is written.

This flips the vibe coding script. Instead of the AI making design decisions on the fly based on whatever prompt you give it in the moment, you lay out the game plan first.

"Choose to be 'navigator or pilot'"

With a spec in hand, you remain in the driver’s seat. You decide the architecture, the components needed, the constraints. The AI follows that map.

The Process

Practically, spec-driven development means investing time up front to write down what you’re building. The workflow looks like this:

1. Collaborative Planning: You and the AI work together to create a specification. I use Claude as a sounding board to help flesh out product requirements, user flows, and technical considerations.

2. Spec Creation: This becomes a living document covering core features, data models, UI flows, and technical choices. The AI can help draft this, suggesting features and considerations you might miss.

3. Structured Implementation: Once the spec is ready, the coding phase begins. The AI now has a blueprint to refer to, turning it from a mystical code generator into an assistant engineer executing a plan.

Modern AI dev tools are increasingly built around this approach. Amazon’s new AI-powered IDE Kiro, for example, asks whether you want to start with a spec or with prompts. If you provide a spec, Kiro’s AI agents break it down into structured artifacts and generate implementation tasks.

The interactive aspect remains: you still chat with the AI, refine the spec together, and iteratively implement it. Developing the spec becomes a creative brainstorming process where you weigh options before any code is written.

Why Specs Make a Difference

Adopting a spec-first approach fixed many of the issues I hit with vibe coding. Here are the concrete benefits I experienced:

Better AI Context and Consistency

Instead of treating each prompt in isolation, the AI now had a persistent reference containing the high-level picture. Misunderstandings dropped dramatically.

For example, when generating a feature, the AI could consult the spec and see “oh, we’re using Postgres and have these three data models,” so it would stop hallucinating dummy data structures or unnecessary classes.

In the vibe days I’d often get code that didn’t quite mesh with what we already built, because the AI forgot or never knew the bigger plan. With a spec, the AI’s responses became more consistent and on-target with the overall design.

Complex Tasks Become Manageable

If I needed a major new module, I could outline it in the spec (or update the spec) and then let the AI implement it in one go or a few systematic steps.

Since the requirements and constraints were spelled out, the AI was able to handle bigger chunks without constant intervention. In vibe coding, asking for a large feature often went off the rails. The AI might start coding something that didn’t fit with the rest of the system, and I’d have to intervene repeatedly.

Now it was more like: “Here’s the plan as we agreed in the spec, please implement it.”

The creators of Kiro note that this approach allows their AI to “implement more complex tasks in fewer shots.” I found this to be true; there were cases where I braced for the usual back-and-forth of corrections, but the AI delivered a correct (or at least mostly correct) solution on the first try, guided by the spec.

Explicit Decision Tracking

The spec document itself becomes a living artifact of the project’s intentions. I got into the habit of committing the spec into source control alongside the code.

This way, when I (or anyone on the team) reviews a pull request, we can see the context: “Oh, this function was written to fulfill requirement X in the spec.” It’s much easier to discuss changes or onboard someone new when you have this reference.

I update the spec file whenever I make manual code changes, to keep the spec and code from drifting out of sync. That might sound like extra work, but it prevents confusion down the line. The spec becomes the single source of truth.

Better Engineering Decisions

There’s a subtle cultural shift when using spec-driven development: it encourages thinking before coding. Forcing myself (and the AI) to articulate the plan first led to better engineering decisions. We caught potential issues early on (like noticing “hey, if we do it this way, scaling to multiple users will be hard. Maybe choose a different approach now”).

In vibe mode, we likely would have barreled ahead and hit those issues only when the system broke.

More Cohesive Results

When the code started coming together, it was far more cohesive. The features integrated smoothly, because they all traced back to the same spec. No more sudden surprises like a function that doesn’t talk to the database because the AI didn’t realize we were using an ORM (yes, that happened in the purely vibed version).

If vibe coding felt like jazz improvisation, spec-driven development felt like jamming with a chord progression in mind. There was still room to be creative and adapt on the fly, but there was structure to prevent total chaos.

From Prototype to Production, with Confidence

After adopting spec-driven development, I noticed a dramatic improvement in the production-readiness of AI-assisted code. The difference was night and day when deploying the AI-written code.

Performance issues were reduced. In the vibe-coded prototype of my app, I discovered one page was making dozens of redundant database queries. After switching to spec-driven, we included a note in the spec like “this feature needs to handle ~1000 records efficiently,” and the AI then suggested using pagination and proper indexing in the code.

Perhaps the best part is that it makes collaboration with other humans easier. Because the spec and the code are aligned, my team could follow along with what the AI was doing. We could do code reviews against the spec: “The spec said it should do X, is the code doing X?”

In enterprise environments, that kind of traceability and documentation isn’t just a luxury; it’s often mandatory. Spec-driven development provides that paper trail of reasoning as a natural byproduct of the process.

Wrapping Up

Switching from pure vibe coding to a spec-driven approach was a revelation for me. I got to keep the thrilling parts of AI-assisted coding: the speed, the flexibility, the “wow it just wrote that!” moments. But I mitigated the chaos and uncertainty that previously came with it.

To be clear, vibe coding still has its place. If I’m prototyping a tiny script or experimenting, I might not bother writing a full spec. But the moment I’m building something that others will use or that I’ll need to maintain, spec-driven development is my go-to.

"The vibe felt good. The reality didn't scale."

Spec-driven development marries the creative speed of AI “vibe” coding with the solid planning of traditional development. You don’t lose the magic; you just contain it in a useful form.

It’s the best of both worlds: the vibe and the spec working in harmony.