Embracing the “Vibe” in Vibe Coding: How to Work Faster Without Losing Control

Author(s): Prineet Kaur 👩‍💻

Originally published on Towards AI.

Ijust describe what I want, run, tweak, repeat. — A rough paraphrase of how many newbie developers are now approaching “vibe coding”

We’ve all been reading articles lately about “vibe coding” — a trend where code creation is less about writing every line and more about guiding AI tools through intent, feedback, and iteration.

If traditional programming is about precision, vibe coding is about intent, feedback, and flow. You describe what you want, let the AI generate it, and then steer, correct, and harden it until it fits.

But what works well, what doesn’t, and how should developers adopt it responsibly?

Let’s explore 🚀

1. What’s the hype around this “Vibe Coding”?

At its core, vibe coding means developing software by communicating intent to an AI system — describing what to build, reviewing how it creates it, and refining until it feels right.

Instead of writing every function from scratch, developers act as curators or conductors, using AI tools to scaffold the work. The result is faster iteration and an expanded creative range.

This approach doesn’t remove engineering skill — it redefines it. You still need to understand architecture, testing, and debugging. But your focus shifts from typing syntax to directing behavior.

👉 Vibe coding does not mean zero-coding. It’s AI-guided coding, where your role transitions from micromanaging details to curating, correcting, and orchestrating.

2. Diving into the Vibe Coding Mindset

Vibe coding follows a simple workflow loop, which can be summarized as:

1. Describe your intent clearly — “I need, e.g., a Metrics API that reports daily usage.”
2. Let the AI scaffold your project — it instantly provides you with the initial code skeleton (e.g., folder structure, config files, and environment setup)
3. Run and observe — Execute it, review logs, identify gaps.
4. Refine through feedback — Adjust prompts, ask for tests, or tweak logic.
5. Validate and integrate — Add tests, polish naming, and ensure security.

👉 A key insight: you’re not just “getting the AI to code” — you’re steering the AI, validating, pruning, and integrating. The expertise shifts.

3. Hands-On Example of Vibe Coding in Practice

Let’s see how vibe coding works in practice with a small project.

Let’s walk through a small project example of “Metrics API”

Goal as a Developer: Create an endpoint that returns basic usage metrics for an account (requests per day, errors, latency).

Step A: Describe the Intent (Prompting the AI)

Sample Prompt could be ⤵️

“Build a Node.js Express route /metrics/usage that returns a JSON object with today’s request count, error count, and average latency. Include error handling.”

Step B: Run & Observe (Reviewing the AI Output)

Expect a generated stub* like below ⤵️

(*basic version of the code, which will have the right shape and structure, but only minimal or fake logic inside)

app.get('/metrics/usage', async (req, res) => {
 try {
 const usage = await getUsageData(req.user.id);
 res.json({
 requests_today: usage.requests,
 errors: usage.errors,
 latency_ms: usage.latency
 });
 } catch (err) {
 res.status(500).json({ error: 'Internal Server Error' });
 }
});

It’s functional, but you notice missing authentication and defaults.

Step C: Iterate (Prompt Refinement)

Sample Prompt ⤵️

“Add a 401 response if no user is logged in, and default counts to zero if data is missing.”

Sample Revised output after the above:

app.get('/metrics/usage', async (req, res) => {
 if (!req.user) return res.status(401).json({ error: 'Unauthorized' });
 
 try {
 const usage = await getUsageData(req.user.id) || { requests: 0, errors: 0, latency: 0 };
 res.json(usage);
 } catch (err) {
 res.status(500).json({ error: 'Internal Server Error' });
 }
});

Step D: Validate and Integrate (Improving Security)

Sample Prompt ⤵️

“Write a Unit test for this endpoint.”

The AI drafts a test, you refine it, validate it & incorporate it into your code.

👉 That’s vibe coding in motion — intent, iteration, validation.

4. Now, let’s talk about the Risks with Vibe Coding

While vibe coding can accelerate development, it’s not always the right approach.

Common Pitfalls When Vibe Coding

Even with AI in your toolkit, it’s easy to slip into bad habits. Here are the traps most developers hit — and how to dodge them:

1. Overreliance on AI output: The code may appear clean, but it conceals edge-case bugs or security vulnerabilities. Treat every AI commit like a junior teammate’s PR — review it carefully.
2. Fragmented architecture: Mixing snippets from different prompts without structure leads to messy, inconsistent systems. Keep a clear architecture in mind, not just a collection of “cool outputs.”
3. Prompt fatigue: Rewriting prompts endlessly instead of refining your project’s design wastes time. Focus on clarity, not cleverness.
4. Harder debugging: If you didn’t write the logic yourself, tracing failures can feel like spelunking in someone else’s brain. Add logs, tests, and comments early.
5. Drifting code style: AI tools don’t always adhere to your team’s coding conventions. Utilize linters, formatters, reviews to maintain coherence.
6. Scaling surprises: What works fine in a demo may collapse at scale. As projects grow, revisit AI-generated sections and refactor with intent.
7. Ethical and licensing blind spots: Some AI outputs can reproduce licensed code or biased patterns. Always check, clean, and own your final implementation.

When Not to Use Vibe Coding

Vibe coding isn’t a silver bullet. There are moments when you need precision, not automation. Avoid it when working on:

→ Security-critical systems — Anything touching authentication, encryption, or sensitive data deserves hand-written, peer-reviewed code.

→ Performance-sensitive paths — Real-time processing, memory optimization, or hardware-level logic still need human-tuned craftsmanship.

→ Legacy systems with fragile dependencies — One wrong abstraction can ripple through the stack. Manual control beats AI shortcuts here.

→ Core business logic — The modules your team must fully understand and maintain for years should be written deliberately, not prompted.

👉 In such cases, traditional, hand-coded, peer-reviewed work still wins.

5. Finally: “How to Embrace the Vibe in Vibe Coding”

Here’s how to make the most of vibe coding without losing control:

• Start small — Use it for scripts, utilities, and prototypes before core systems.
• Be intentional with prompts — Think of them as mini-specifications.
• Add tests early — Treat tests as part of the conversation.
• Enforce structure — Apply your project’s style guides and architecture.
• Review like a teammate — AI code still deserves human review.
• Track what works — Save good prompts, reuse them, and build an internal playbook.

👉 The more structure and feedback you bring, the better the “vibe” becomes.

6. The Bigger Picture: Take Away 🙌

Vibe coding isn’t about replacing developers — it’s about expanding what developers can do. It frees you from boilerplate and repetition, letting you focus on flow, design, and creative problem-solving.

While you still need technical judgment, you apply it earlier and faster 🚀

The skill of the future isn’t just “knowing syntax” — it’s knowing how to guide intelligent tools toward reliable outcomes.

👉 Question for thought: “How far do you trust AI in your workflow today — and where do you still draw the line?”

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Looking forward to connecting with you to share my learnings 🤝

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Published via Towards AI