I'm excited to introduce today's guest expert: Shivam Srivastava. He's a Tech Lead and writer with nearly a decade of experience designing and building scalable applications using Java, Spring Boot, Microservices, and SQL. He has also mentored hundreds of developers in their interview prep journeys.
Today Shivam is here to cover a topic that has been top of mind for all of us lately: Vibe Coding. He'll discuss how to leverage AI effectively — and how to avoid the most common traps that inexperienced developers fall into. This is great advice, and I think you'll get a lot out of it.
Now over to Shivam!
Hi everyone, I’m Shivam 👋
"Vibe coding" is a term popularised by AI expert Andrej Karpathy to describe coding by relying on the vibe of AI suggestions rather than a solid understanding. In essence, a vibe coder will throw a vague prompt at an AI (or copy code from Stack Overflow) and paste the generated solution straight into their project. It might work initially, but this approach is a ticking time bomb.
Picture this: It’s late evening, and a developer is frantically trying to close a ticket. They open their IDE, copy an AI-generated snippet, paste it, and — boom — it compiles.
The test seems fine, they push the code, and the ticket is closed.
Success? Not really.
The next morning, a bug pops up. Nobody knows how the logic actually works. The vibe coder shrugs — they just trusted the AI.
Now the whole team is stuck unravelling spaghetti logic that nobody truly understands.
That’s the difference between a vibe coder and a real developer.
A vibe coder rides the surface, letting AI (or earlier, Stack Overflow) do all the thinking. A real developer uses AI as leverage while sharpening their own critical thinking and problem-solving skills.
If you don’t want to be another vibe coder, you need to flip your relationship with AI. Treat it as a tool that clears your mental bandwidth so you can focus on the real work: critical thinking and problem-solving.
The Problem With Being a Vibe Coder
On the surface, vibe coders look like they’re getting the job done:
They generate some code.
It sometimes compiles and sometimes even runs.
The ticket might get closed — for now.
But in reality, here’s what often happens:
The AI-generated code doesn’t always work as expected.
Bugs creep in because nobody validated the logic.
When requirements shift, they have no clue how to adapt the code.
They can’t explain why a certain solution was chosen, because they didn’t think it through in the first place.
That’s the danger — you’re shipping code you don’t fully understand. And in a world where AI is getting better every month, the developers who just copy-paste outputs will quickly be outpaced by those who actually think.
Why Critical Thinking Is Your Edge
Here’s the truth: AI can suggest code, but it doesn’t know your business domain. It doesn’t understand:
The trade-offs between performance and maintainability.
How regulatory requirements might affect your design.
Why a certain architectural choice could save or cost millions long-term.
That’s where you come in. Your edge as a developer isn’t typing speed or memorising syntax — it’s how well you can frame problems, think critically, and make decisions AI cannot.
Using AI the Smart Way
Think of AI as your junior developer — fast, eager, but lacking judgment. Your job is to manage it effectively. Here’s how:
Outsource the repetitive fluff Boilerplate code, CRUD operations, basic test cases — let AI handle those. Don’t burn your mental energy on scaffolding.
Keep ownership of the complex logic Anything involving nuanced business rules, critical integrations, or performance-sensitive code should be your territory. This is where you build irreplaceable expertise.
Use AI for exploration, not decision-making Stuck on a problem? Ask AI for approaches. But the choice of which path to take — and why — should be yours.
Turn AI into a sparring partner Explain your reasoning to it. Challenge its output. Often, just articulating your thought process exposes gaps in your logic — and sharpens your understanding.
Verify, always AI output is not production-ready code. Run tests. Debug thoroughly. A vibe coder copies and ships; a real developer inspects, questions, and validates.
Building Problem-Solving Muscles
Think about what separates a great developer from an average one.
It’s not typing speed. It’s not memorising every method in Java or Python.
It’s the ability to:
Break a complex business problem into smaller solvable chunks.
Anticipate edge cases before they blow up in production.
See how today’s decisions affect scalability and maintainability tomorrow.
AI can accelerate your learning curve, but it won’t do the heavy lifting of building these muscles. Only consistent practice, reflection, and deliberate problem-solving will.
This way, you’re not outsourcing your brain. You’re amplifying it.
Here are a few habits that can make AI a multiplier instead of a crutch:
Before asking AI, outline your own approach. Even if you’re unsure, sketch your logic first. This forces you to think.
Use AI for syntax, not strategy. It’s great at generating code structure but weak at higher-level reasoning.
Re-engineer AI code. Don’t just accept its output. Refactor it. Optimize it. Ask yourself: “How would I make this better?”
Keep a problem journal. Note tricky bugs or design challenges you solved. Over time, this becomes your personal playbook.
Final Thoughts
If you’re using AI as a replacement for your brain, you’re just a vibe coder with shinier tools. But if you combine AI’s speed with your critical thinking, you become unstoppable: someone who solves complex problems faster, more creatively, and with a deeper understanding.
AI will only get better. The real question is — will you?
If you enjoyed this, you can find my insights on technology, career growth, and personal consistency on Quora and Medium.
