Example Driven Development: An Antidote for the Age of AI-Assisted Vibe Coding

A move away from guesswork, towards clarity

In a world where large language models gladly conjure up code at the mere hint of a prompt, we’ve entered a new era: AI-assisted vibe coding. The phrase, coined by Andrej Karpathy, captures a feeling countless developers recognize; describing what you want in natural language, trusting an LLM to translate that into code, then nudging and poking until it does what you meant. It’s a liberating, exhilarating, sometimes bewildering creative process. But as the “vibes” take over, something subtle and powerful is needed if we don’t want to end up with a tangled, mysterious codebase no one understands.

I believe the answer is Example Driven Development (EDD). Here’s why this style; rooted in clarity, emergence, and real-world behavior matters more than ever.

When “Just Vibe It” Needs a Guide Rope

There’s genuine productivity in vibe coding. Describe, iterate, accept the AI’s help. Yet time and again, I’ve seen the excitement shift into uncertainty:

• Did the AI get my intent right?
• Am I still in control of what’s being built?
• What actually matters in this behavior I’m seeing?
• Three months later: Who understands this thing?

This is where examples shine brighter than ever.

What is Example Driven Development?

If Test-Driven Development (TDD) invites us to start with tests — assertions of behavior, EDD urges an even closer connection to real use. You don’t just write tests that pass or fail; you write examples that create and return tangible, inspectable objects. These become living artifacts; inspectable, explorable, reusable.

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In essence:

• An example simulates a real, meaningful use of the system.
• It isn’t just a “test”, it’s an explicit, concrete snapshot of how your code should work.
• These examples are not only checked “Does it pass?”, but can be explored, composed, and evolved.

Why Examples? Why Now?

AI-assisted coding means more is generated, faster, with less direct developer oversight. When you vibe it, context (nuance, assumption, intent) can get lost. By anchoring development in rich examples, you achieve:

• Clarity: Everyone (AI and human) aligns around concrete, real-world usage cases.
• Transparency: Examples document both intent and functionality, becoming “living documentation.”
• Emergence over guesswork: Instead of guessing the right assertion or setup, you let examples emerge from genuine exploration.
• Reduced technical debt: You return to examples to verify new features or understand breakages; less “hunt the bug in generated code.”

EDD in the Workflow (With AI in the Loop)

So how does this flow look in the new world?

1. Start with a real example. Describe or model how someone would actually use your feature, not just how it “should” work in the abstract.
2. Use AI as an assistant. Feed this example to your AI coding tool — don’t ask it to “just code X feature” but to help build or extend the example. Let it generate object creation, edge cases, even the exploratory steps.
3. Inspect, iterate, and extract. As you run these examples, see what comes back. Instead of only checking green/red tests, you’re reviewing actual objects, exploring their state. New insights emerge; new examples take shape.
4. Grow your suite of living, evolving examples. Each becomes a checkpoint for future features, refactors, or extensions.

But Isn’t This Just TDD/BDD/ATDD?

Good question! EDD owes plenty to its cousins, but with a crucial twist: the focus is on the richness and reusability of example objects, not just test assertions or “given/when/then” statements. In the AI era, these are not mere “contracts” but deep, shared reference points for both human creativity and machine assistance.

AI-Assisted? Even More Reason to be Example-First

With so much of our code now being synthesized, it’s tempting to skip this rigor and vibe along. But the stakes are higher. AI gives us acceleration; EDD ensures direction. When you show your AI an example, you’re feeding it ground truth; teaching the machine and your human peers how your system is meant to work.

You’re not just asking “does it work?” but “does it work for this real, meaningful case?” And when the AI improvises, you’re never far from an anchor: you always have your examples to guide, correct, and elaborate as both you and the AI learn together.

Final Thoughts: Living in a Vibe-Driven World; and What I’ve Learned

AI has democratized development, lowering the barriers to building, remixing, and iterating. But I’ve found that if we truly want our code; and our teams; to thrive, we have to marry this velocity with disciplined clarity. Example Driven Development is more than a method. It’s a game-changer, and here’s why I say that from personal experience:

1. Critique and Refine, Not Just “Generate and Ship”

Having AI generate code is exhilarating; but examples turn the process into a true dialogue. Each example becomes a litmus test; AI-generated code gets critiqued, edge cases fall out, and scenarios become clearer. I’ve felt my role shift from “code producer” to “scenario sculptor and critic,” and that’s led to smarter, more resilient features.

2. Security and Compliance, Shifted Left

By weaving security and compliance scenarios right into your examples, you don’t bolt these concerns on after the fact. The “shift left” philosophy becomes real: The AI learns to anticipate vulnerabilities and policy guardrails from the very first example, not the eleventh-hour audit. I’ve seen compliance signoff move from late-game blockers to natural, early accelerators.

3. Robustness and Velocity: Near Zero Defects, Faster Cycle Times

EDD doesn’t just help you surface bugs; it helps you avoid them. I’ve consistently created code that is robust, with near zero defects before hitting a pull request. This preemptive clarity leads to faster reviews, fewer painful hotfixes, and happier teams. Cycle times shrink, trust soars.

4. AI Truly Performs Best With Examples; EDD Unlocks Its Power

I call this approach Example Driven Development for a reason: the richer and clearer your examples, the better AI assists you. Its suggestions get sharper, its “imagination” more aligned with your goals. In my hands, EDD has made AI a true partner, not just a fast-forgetting assistant.

5. Becoming “Beyond Full Stack” — Turning Ideas Into Value in Days “without prior experience/exposure”

There’s a new breed of developer emerging; those who can take an idea all the way to a living, cash-generating product, sometimes in just days. What’s truly revolutionary is how AI obliterates the traditional entry barriers: you no longer have to spend months mastering a new language or framework before building something impactful. With a good AI assistant, you describe your intent or your example, and suddenly you’re shipping features in a tech stack you just discovered last week. EDD, paired with AI, lets you leapfrog domain silos, erase technical intimidation, and focus on what you want to create — not the hurdles of how. It’s no exaggeration: today, with the right tools, you can transcend the “full stack” and simply become a builder. The power to go from thought to reality to monetization, to value is finally in your hands, and I’m seeing it firsthand.

In short, Example Driven Development especially in the AI-assisted, vibe-centric world has made me not just a faster coder, but a smarter, more holistic one. It’s the difference between vibing aimlessly and building something built to last, built to scale, and built with intent. Let’s make our examples not just a safety net, but our creative springboard. The future’s here. Let’s code it together, with intent and clarity.