Social media feeds are currently split into two extremes. On one side, people claim anyone can build a software startup in a weekend without writing a single line of code. On the other side, developers complain that vibe coding produces unmaintainable spaghetti code and endless debugging loops.
Both perspectives miss the core reality.
Building real, reliable automations and applications with AI is not about prompt tricks or hoping the model understands vague ideas. It comes down to two classic, pre-AI skills: engineering intuition and clear task specification.
The Debugging Edge: Knowing “Why” Immediately
When you build with AI coding agents, intermediate failures are part of the process. The difference between an engineer and a beginner is what happens when something breaks.
An experienced developer looks at an unexpected error or incorrect behavior and immediately recognizes the root cause:
- Did the model hallucinate an API parameter?
- Is there a race condition or unhandled async state?
- Did the data schema change unexpectedly?
Because you understand how systems work under the hood, you do not ask the AI to blindly “fix the bug” five times in a row. You pinpoint the exact architectural failure and instruct the model on how to solve it properly so you never have to revisit it.
Setting the Guardrails Before Generating Code
Most failed AI projects fail before the first file is created.
When you outline a project to an AI agent, thinking through architectural decisions early on saves days of refactoring:
- Data storage strategy: Should data live in a relational database, local flat files, or stay in-memory?
- API limits and safeguards: Setting rate limits, pagination caps, and token thresholds so automated test loops do not burn through budgets.
- Error handling and resilience: How the system behaves when an external webhook drops or times out.
An experienced engineer defines these constraints upfront. The AI does not have to guess; it simply executes within clear boundaries.
The Management Skill: The Art of the Spec
This dynamic is not limited to developers. Strong managers and technical leads have a massive advantage with AI.
Long before LLMs existed, great managers knew how to break down complex business goals into unambiguous, actionable specifications. If a manager wrote clear user stories and acceptance criteria, human developers delivered solid results without endless back-and-forth.
With AI agents, that exact skill becomes a superpower.
When working with clients who know how to articulate their requirements clearly, it is pure joy. Sometimes their task description can be pasted directly into an AI agent prompt, producing working software on the very first run.
Summary
Vibe coding does not make technical thinking obsolete. It amplifies it.
If you know how software works and how to clearly state what you want to achieve, AI tools will make you dozens of times faster. If you treat AI like a magical black box that will figure out your product for you, you will spend your days trapped in debugging loops.
ilf.studio — AI-native web studio, Gdansk, Poland.
