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Stop Vibe Coding: How to Actually Engineer with AI

Swapnanil Ray
August 29, 2025
Stop Vibe Coding: How to Actually Engineer with AI

AI-assisted coding is no longer science fiction — it’s our daily reality. Tools like Github Copilot, ChatGPT, Claude Code, Cursor, Windsurf (it’s an endless list, so I’ll stop here) are redefining how software developers write code.

But with this revolution, something interesting has emerged: NOT everyone is using these tools the same way or with the same intentions.


Some (if not most to be fair) so-called developers — I might be in trouble if I call them wanna-be devs — use AI like a genie:

“Build me a 3D game that makes me $$$.”

While the rest of the pack uses it like a self-hired junior developer — a helper who speeds up the work, but still needs oversight, reviews, and integration into proper engineering practices.

This is the difference between Vibe Coding and Vibe Engineering.


It’s not just semantics. This difference determines whether your AI-assisted project becomes:

  • a fragile toy prototype (dreaming of an 8-figure bank account), or
  • a production-ready, highly stable system delivered faster than usual timelines.

What is Vibe Coding?

Vibe coding is what happens when someone treats AI like Doraemon’s 4D Pocket or a magic wand. Instead of understanding the problem and designing a suitable solution, the vibe coder just throws vague instructions at the AI:

“Build me a 3D game with multiplayer!” “Write me an entire backend in Rust!” “Generate a SaaS app with payments and auth!”

The AI — being a solid stickler for instructions — dutifully spits something out. Code that looks real, compiles in some cases, but is often filled with silent bugs, security holes, or straight-up architectural catastrophes.

The vibe coder celebrates… until the code breaks, the project collapses, and nothing makes sense anymore.

Why is it dangerous?

  • AI doesn’t truly understand context.
  • It often invents libraries, methods, configs that don’t exist.
  • It confidently produces wrong solutions.
  • AI has no sense of business risk — you do.

📉 Real Example: In July 2025, an AI coding agent from Replit deleted a live database during a code freeze in a SaaS company. The AI admitted to running unauthorized commands, violating explicit instructions. → AI Coding Tool Replit Wiped Database

This highlights the risk: AI might know your codebase but will never know your business criticality. Lack of supervision = disaster.

👉 Essence of vibe coding: Blind trust in AI with no clue about your systems, constraints, or consequences. Fun, fast, reckless — but catastrophic.


What is Vibe Engineering?

If vibe coding is blind trust, vibe engineering is the exact opposite.

It’s about using AI as a tool, not a crutch. A vibe engineer treats AI like a powerful but naive intern: fast, enthusiastic, but prone to mistakes if unsupervised.

Instead of saying:

“Hey, build me an entire game from scratch”

The vibe engineer says:

"Generate the boilerplate for a REST API in TS.”
“Suggest unit tests and edge cases for this workflow.”
“Help me refactor this routine to be more efficient.”

The vibe engineer knows the big picture and uses AI to accelerate — not delegate.

Vibe Engineering in Practice

  1. Define the system architecture (APIs, data model, infra, design patterns).
  2. Break work into small, testable prompts (not vague mega-prompts).
  3. Validate everything — run tests, check logs, benchmark.
  4. Use AI to learn new tech, not to blindly build.

Why vibe engineering works:

  • You stay in control.
  • AI reduces grunt work, freeing you for design & architecture.
  • Reviewing AI code fosters exponential growth.
  • The outcome: Scalable, durable, and safe systems.

Pro Tips: How to be a Great Vibe Engineer

1. Treat AI like a Junior Engineer

Think of AI as an eager teammate who loves to code and is very eager (and writes good code as well). It can draft code really fast, but doesn’t understand business logic, or long-term consequences. You own that. You own the decision.

2. Break Problems into Bite-Sized Prompts

Don’t throw a vague request to the AI. Instead decompose your requirements into multiple bite sized prompts that are easy to digest, not just for the AI, but for you as well — essentially giving you a better control and ensures you understand everything.

3. Validate Everything

AI code is unreviewed code. Even though it might have context of your entire codebase, yet it is insufficient to be committed. You’d have to make a practice to write tests, run analysis and linters, review code for security flaws and check for unecessary dependencies that the AI might introduce.

Never merge blindly.

4. Keep the Human in the Loop

Yes, let AI do all the grunt work. But never let it decide on your architecture & design, critical business logic, dependencies and approaches.

5. Learn from the Output

Every suggestion that the AI churns out, is an opportunity to learn. You should be asking it follow-up questions on approaches that it took, on pattern decisions that it made. Don’t just copy-paste, grow with the AI.

6. Place Guardrails

AI should NEVER have access to your Production environment, let alone your Production Database. Making sure that any scripts or optimization approaches never reach the production before a rally of testings and scans through the pipelines.

7. Document and Share

One thing that I learned from a colleague / friend is to use AI to generate READMEs, diagrams, documentations first — before allowing it to touch any parts of your code. That way you would have clarity on what needs to be done and your future self will obviously thank you for not leaving them surprises.

Being a great engineer (and a vibe engineer) is about having the control, disciple and curiosity about your work. AI is the means to make you have those even more, not surrender to it.


Final Thoughts

AI-assisted coding is here to stay. The question is no longer “Should we use AI for software development?”, there’s no shame honestly in using AI to aid your work, — rather the question is “How should we use it?”

At the end of the day, AI can write code. But, only you can build systems.