Prompt Driven Development: Moving Beyond Vibe Coding

 


AI-assisted coding has taken the software world by storm. You’ve probably already experimented with tools like Copilot, Claude, or Cursor. Maybe you’ve even tried “vibe coding” — typing vague prompts and letting the AI fill in the gaps.

But as these tools evolve, so must our approach. The future isn’t just vibe coding. It’s Prompt Driven Development (PDD) — a structured, intentional way to guide AI coding assistants.

Let’s walk through what that shift looks like, why it matters, and how you can start practicing it today.

From Vibe Coding to Prompt-Driven Development

1. Vibe Coding

At first, AI-assisted coding felt like magic. You typed a few words or half a thought, and the AI tried to complete it. Sometimes it nailed it, other times it wandered.

  • Strengths: Fast experiments, fun discovery.

  • Weaknesses: Inconsistent results, lack of repeatability, and hard to debug later.

Think of it like jamming on a guitar: it’s creative, but messy.

2. Prompt Coding

The next step forward is prompt coding. Here, you’re more deliberate with your requests. Instead of “make me a login system,” you break it down:

  • “Create a controller with a login endpoint.”

  • “Generate a Blazor component with email/password fields.”

  • “Write unit tests for the login service.”

It’s more structured than vibe coding, but still ad hoc. You’re talking to the AI like a junior developer — giving tasks, but not a full roadmap.

3. Prompt Driven Development (PDD)

Prompt Driven Development takes this to the next level. It’s not just about single prompts. It’s about using prompts as part of your software development process.

Here’s what it looks like in practice:

  • Plan with prompts

  • Develop in slices

  • Test continuously

  • Commit often

  • Refine prompts

Let’s break these stages down with real-world sample prompts.

Stage 1: Plan with Prompts

Instead of writing requirements in your head, use the AI as a planning partner.

Example Prompt:

“Act as a product owner. I want to add user login to my Blazor app. Generate 3 user stories with acceptance criteria. Format in Gherkin style.”

AI Output (sample):

  • User Story: As a user, I want to log in with email/password so I can access my account.

  • Acceptance Criteria:

    • Given that I am on the login page, when I enter valid credentials, I am redirected to the dashboard.

    • Given that I entered invalid credentials, I see an error message.

You now have testable requirements before a single line of code is written.

Stage 2: Develop in Slices

Don’t ask the AI for the whole login system at once. Slice it up.

Example Prompt (Backend):

“Generate a C# ASP.NET Core minimal API endpoint for POST /api/login that takes email and password, validates against a fake in-memory user store, and returns a JWT token.”

Example Prompt (Frontend):

“Create a Blazor Login.razor component with two input fields (email, password), a login button, and a call to the /api/login endpoint. Display errors when login fails.”

Each slice is small, testable, and easy to refine.

Stage 3: Test Continuously

Testing is where AI really shines — it can generate both happy-path and edge-case tests.

Example Prompt:

“Write xUnit tests for the LoginService. Cover:

  • Successful login with valid credentials.

  • Invalid password.

  • Unknown email.

  • Empty email/password.”

This ensures that every code slice comes with guardrails.

Stage 4: Commit Often

Even with AI-generated code, your workflow matters.

Example Prompt (for documentation):

“Write a Git commit message summarizing the changes to the login feature in conventional commit style.”

This keeps your history clean and understandable for the future you (and your teammates).

Stage 5: Refine and Reuse Prompts

Don’t throw away your best prompts. Treat them like coding assets.

  • Save them in a prompt-library.md file.

  • Tag them by use case (planning, frontend, backend, testing).

  • Reuse and refine as your project grows.

Example Prompt (meta):

“Rewrite this prompt so it’s clearer and more reusable across multiple projects.”

Why This Shift Matters

You already know that AI is getting smarter. But smarter tools don’t automatically mean better software. Without a process, you’ll get bloated code, hidden bugs, and wasted time.

With Prompt Driven Development, you:

  • Gain consistency in results.

  • Reduce wasted debugging cycles.

  • Build maintainable, testable software.

  • Treat prompts as a new kind of coding artifact.

The Road Ahead

As Copilot, Claude, Cursor, and other AI coding partners evolve, so must your workflow. Vibe coding was fun. Prompt coding gave us control. Now, Prompt Driven Development promises consistency, speed, and quality.

The takeaway? Don’t just write code with AI. Develop with AI.

Call to Action

You’ve probably had your own experiences — both good and frustrating — with AI-assisted coding. How are you moving beyond vibe coding? Share your thoughts and stories in the comments below.

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