As a full stack developer currently exploring AI/ML, I’ve been building projects in a traditional way: choosing the tech stack, writing code, and solving problems step by step.
Recently, I came across the term Vibe Coding, and it instantly caught my attention. It felt very different from how I usually work, but I was curious. So, I gave it a try.
What follows is my experience doing both vibe coding and professional coding, what I learned from each, and where I think they stand in today’s world of development.
💡 What is Vibe Coding?
Vibe Coding is when someone builds a project using only AI tools like ChatGPT, Gemini, or Claude without needing deep coding skills.
You give the idea, and the AI does the rest: picks the tech stack, writes code, creates folder structures, and even helps with deployment. You’re mainly guiding the AI with prompts.
Vibe coders don’t write code - they guide it.
🧑💻 What is Professional Coding?
Professional Coding is the traditional way of building software using your own skills: choosing tools, writing and debugging code, and understanding how everything works.
You control every part of the process, from idea to deployment, with a clear focus on performance, security, and maintainability.
Professional coders don’t copy solutions they build understanding.
My Experience Doing Vibe Coding Projects
After hearing the term Vibe Coding, I couldn’t ignore it. The idea of building entire projects without manually writing code felt wild, but I was curious. As a developer, I’ve always believed in understanding what I build. Still, I wanted to test this hands-on.
So, I built two complete projects using only ChatGPT.
Here’s the rule I gave myself:
“I will only share the idea or problem statement. Everything else from tech stack to deployment should come from the LLM. No using my own developer knowledge, no second-guessing. Just follow instructions like a non-coder would.”
💡 The Process
I gave ChatGPT the problem I wanted to solve. Then it started spitting out everything:
- A list of project name suggestions
- A complete folder structure
- Fully-written UI code for the entire front end
- Step-by-step backend logic
- Setup for database connection
- Final deployment instructions
And surprisingly, it worked. I followed the steps, copy-pasted the code, made minor edits, and got both projects live. It felt productive. I didn’t have to think hard. It was fast.
But then, I noticed something…
🤯 What I Really Learned
This section is your goldmine, so let’s break it down with clarity and impact.
1. 🔧 Customization is the First Wall
As a full stack dev, I’m used to tweaking layouts, styles, and components effortlessly. But with LLM-generated code, customization becomes a chore.
Want to slightly change button styles? Need a complex layout shift? You have to re-explain the whole thing to ChatGPT with perfect clarity. That means more prompting, more trial-and-error, and slower iterations.
LLMs give you a generic solution not your solution.
2. 🔐 Security is a Mirage
One of the biggest issues I noticed was in authentication and security. LLMs gave me working login/signup flows — but they were shallow.
Things like:
- No proper token expiration
- No brute force protection
- Poor validation logic
- Weak password handling
If I had shipped that code to a real client, it could’ve been exploited in minutes.
LLMs might give you working code, but not secure code. You still need to think like a dev.
3. 🧠 No Learning = No Growth
Even though I built two projects, I didn’t learn anything new. Why? Because I didn’t think through problems, didn’t explore tradeoffs, didn’t get stuck and debug my way out.
Vibe coding is execution without education.
100 vibe-coded projects ≠ 1 deeply coded, hands-on project.
4. 🐞 Debugging is a Nightmare Without Understanding
The AI-generated code ran, but in some cases the output was wrong and no errors showed up. That’s the scariest type of bug: when it looks like it’s working but it’s not.
Without a real understanding of how the code works underneath, fixing this was pure guessing.
If you can’t debug it, you don’t really own it.
Vibe Coding vs Professional Coding - Pros & Cons
After trying both styles AI-powered vibe coding and hands-on professional coding I’ve seen both the power and pitfalls of each. Let’s break it down.
🤖 Vibe Coding: Pros & Cons
✅ Pros of Vibe Coding
Rapid Development
You can go from idea to deployment insanely fast. If you can describe what you want well, LLMs can generate an entire working solution in minutes.
Example: I built a landing page with authentication, hosted on Netlify all within 30 minutes using just ChatGPT.Low Barrier to Entry
You don’t need a CS degree. High school students or non-tech folks can build apps if they learn how to communicate with the AI effectively.
The real skill here is “Prompt Engineering.” If you can think clearly and give instructions, you can build.Stress-Free Coding
No dealing with tricky syntax, confusing errors, or mental overload. You let the AI handle the heavy lifting, and you just implement.
It feels like autopilot you stay in the flow without friction.
❌ Cons of Vibe Coding
Customization is Frustrating
You hit a wall quickly when you want something highly specific. AI can’t feel your design intentions or exact user flow.
I spent 30 minutes tweaking a layout ChatGPT wrote in 30 seconds.Security is Weak
Most AI-generated code skips over edge cases, validations, and proper auth flows.
LLMs often forget things like token expiry, rate limiting, or password hashing best practices.Zero Conceptual Understanding
You follow the recipe but don’t learn why the ingredients matter. You become reliant on the LLM instead of growing.
After 100 projects, you might still not know how an API works under the hood.Debugging is Hard Without Context
If the output is broken or logic is wrong, and you don’t know how the code works, fixing it becomes trial and error.
In one of my Gen AI projects, I used an LLM chat model that suddenly stopped responding no error messages, no logs. It took me over an hour to even figure out where the issue was.
👨💻 Professional Coding: Pros & Cons
✅ Pros of Professional Coding
Full Control Over the Stack
You understand how everything connects: frontend, backend, APIs, databases. You can adjust anything at any time.
You don’t need to prompt anyone you architect the solution yourself.Reliable Debugging
You understand the system, so you can trace bugs, isolate issues, and fix them quickly.
“I know why this 500 error is happening” beats “ChatGPT said this should work.”Long-Term Skill Growth
Every bug you fix, every feature you write, teaches you something valuable. Your future self becomes 10x stronger.
You’re not just building apps. You’re building mastery.You Become a Creator, Not a Follower
With enough knowledge, you can build your own libraries, tools, and maybe even your own LLM.
You shift from consuming tech to creating it.You Can Teach Others
Real understanding allows you to help others grow, too. That’s where leadership and opportunity grow.
❌ Cons of Professional Coding
Takes More Time
Writing everything manually, choosing the right patterns, and debugging without AI takes longer.
A simple CRUD app might take hours compared to minutes with AI.Mental Load Is High
You feel pressure when things break or deadlines loom. It’s easy to burn out.
“Why is this API not returning data?” becomes your 2AM nightmare.Perfectionism Can Slow You Down
Professionals often overthink, over-optimize, and overbuild when “good enough” would do.
🔍 So Which One’s Better?
That’s the wrong question. The right one is:
How can you combine the best of both?
🧠 Conclusion: Where Vibe Coding Meets Professional Mastery
So, what’s the verdict?
Vibe coding is powerful. Professional coding is essential. But neither is complete without the other.
Here’s the truth I’ve learned through experience:
You can build impressive projects using LLMs like ChatGPT or Gemini. You can skip the boilerplate, the syntax, the setup. But if you don’t understand how things work, you’re not building software you’re just assembling instructions.
Let’s be real:
- If a startup hands you a low-budget project, blindly following ChatGPT might inflate the tech stack or add unnecessary complexity.
- If you copy-paste everything, you risk security flaws, performance issues, and fragile customization.
- And if you rely on AI for every single step, you may appear competent on the surface but crack under real-world challenges.
That’s why I say:
You don’t need to know everything. But you need to know enough to lead the AI, not follow it blindly.
You need to know:
- How web pages are structured.
- How to add styles manually and customize them.
- What a tech stack means and why it’s chosen.
- What a deployment pipeline looks like.
- How to check AI-generated code for risks and inefficiencies.
- A basic understanding of the theory behind the logic like why to use linear regression over logistic regression in a given context.
Even if you’re not technical, prompt engineering becomes your gateway skill. It’s not just about “asking ChatGPT what to do.” It’s about crafting precise, strategic prompts to extract meaningful, context-aware output.
💡 Final Thought
We need AI to build faster. But AI still needs us to guide it.
The fear that AI will replace developers? It’s only half-true. AI will replace those who refuse to learn how to use it effectively. But the ones who understand both technology and AI who grasp architecture and can also accelerate with LLMs won’t just survive; they’ll thrive. They’ll ship products faster, solve problems smarter, and unlock opportunities others don’t even see.
I’ll admit sometimes my vibe-coded results didn’t hit the mark. And maybe that’s because my prompting wasn’t strong. But that’s the point: prompting is a skill, just like coding. You need to build it. And even if you rely on AI to code, you still need to understand the basics. That’s the real edge: pairing AI fluency with developer intuition.
✅ If you take away one thing from this blog:
Master the fundamentals. Embrace the AI. Combine both and become a 10x developer.
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📖 Blog by Naresh B. A.
👨💻 Aspiring Full Stack Developer | Passionate about Machine Learning and AI Innovation
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