AI Can Build Applications, But Can It Debug Them?
We're living in an interesting era.
Today, someone with little to no programming experience can open an AI tool, describe an idea, and generate a working application within minutes. What once required weeks of learning and development can now be done through a conversation.
It's impressive.
But there's a difference between generating an application and understanding how it works.
A Real Experience
Yesterday, one of my friends told me he was building an automation tool using Claude.
At first, everything seemed to be going well. The AI generated the code, the application launched, and most of the features appeared to work.
Then a problem appeared.
The automation started making unexpected clicks and performing actions that weren't intended. Naturally, he asked Claude to fix the issue.
The problem was that he only provided screenshots of the behavior. There were no logs, no debugging information, no code explanations, and no additional context.
Without enough information, Claude started making assumptions about the root cause. It suggested multiple fixes, but none of them solved the actual issue.
In other words, it began hallucinating.
After spending hours going back and forth with AI-generated suggestions, he eventually asked me for help.
The Four-Hour Debugging Session
What looked like a simple issue turned into nearly four hours of debugging.
We inspected the automation flow, reviewed the code, traced event handlers, checked selectors, and analyzed how the application interacted with the target website.
Eventually, we identified the actual causes behind the unexpected behavior and fixed them one by one.
The automation tool finally worked as intended.
The interesting part wasn't the bug itself.
The interesting part was realizing that the biggest challenge wasn't generating the code—it was understanding it.
The Missing Piece
AI is excellent at accelerating development.
It can:
- Generate boilerplate code
- Create UI components
- Write API integrations
- Suggest solutions
- Explain concepts
- Speed up development workflows
But when something breaks, AI isn't always enough.
Debugging requires:
- Understanding program flow
- Reading logs
- Analyzing errors
- Identifying root causes
- Testing assumptions
- Knowing how different parts of a system interact
These skills come from learning software development fundamentals.
AI Is a Multiplier, Not a Replacement
Many people treat AI as a replacement for programming knowledge.
In reality, AI works best as a multiplier.
A developer with strong fundamentals can use AI to become significantly more productive.
Someone without those fundamentals may still build something impressive, but when problems appear, they often struggle to identify what's actually wrong.
The more complex the application becomes, the more important those fundamentals become.
Final Thoughts
AI has changed software development forever.
It has lowered barriers, accelerated learning, and made building applications more accessible than ever before.
But software engineering is more than generating code.
Understanding systems, debugging problems, and making informed technical decisions remain essential skills.
AI can write code for you.
But when the application behaves unexpectedly, understanding why it happened is still a human responsibility.
Top comments (1)
This hit hard. I've been exactly that person — AI-generated script breaks, and I have no idea what any line actually does. Pasting error messages back to the same AI that wrote it just goes in circles. The "missing piece" section is spot on.