Artificial intelligence has become a permanent part of modern software development.
Tools like ChatGPT, GitHub Copilot, Claude, Cursor, and others can generate code in seconds that would otherwise take minutes or hours to write manually.
But after recently completing a JavaScript project, I learned an important lesson:
AI-generated code is not the same thing as production-ready code.
The Project
The assignment involved building a superhero directory application using data from a public API.
The application required:
- Data fetching
- Table rendering
- Pagination
- Live search
- Column sorting
- Modal detail views
- URL state persistence
- Performance considerations
At first glance, this seems like the perfect use case for AI assistance.
So I started relying heavily on AI-generated solutions.
The Trap
Every time I encountered a bug, I requested a complete replacement for the affected code.
Examples included:
- Pagination issues
- Search behavior
- Sorting problems
- Modal bugs
- Missing table data
The AI frequently provided code that looked correct.
Sometimes it even sounded confident.
Unfortunately, confidence is not correctness.
Several generated solutions introduced new problems while attempting to solve existing ones.
Examples included:
1. HTML and JavaScript Mismatches
The table rendering function produced fifteen columns of data while the HTML contained only eight table headers.
The result:
- Misaligned data
- Broken sorting
- Confusing UI behavior
The code technically executed, but the application was incorrect.
2. Incorrect Numerical Sorting
Strings like:
- 78 kg
- 100 kg
were being compared alphabetically rather than numerically.
This caused:
100 kg
to appear before
78 kg
because string comparison evaluates "1" before "7".
The correct solution required extracting numeric values before sorting.
3. Regressions
A common pattern emerged:
- Bug identified.
- AI generated fix.
- Original bug disappeared.
- Two new bugs appeared.
This created a cycle where progress felt constant but actual completion remained distant.
What Finally Worked
Instead of asking for another complete rewrite, I began auditing the application manually.
I checked:
- DOM structure
- Event listeners
- Render functions
- Data mappings
- Sort implementations
- API response structure
Within a short period, I found several root causes that had been hidden beneath layers of generated code.
The lesson was simple:
Understanding the system is faster than repeatedly replacing the system.
Where AI Excels
Despite the challenges, AI was still useful.
It helped with:
Boilerplate
Generating repetitive code quickly.
Documentation
Explaining APIs and concepts.
Brainstorming
Suggesting approaches I might not have considered.
Learning
Helping understand unfamiliar patterns.
Used correctly, AI significantly improved productivity.
Where AI Should Not Be Trusted Blindly
Production Logic
Always verify.
Architecture Decisions
Always review.
Security-Critical Code
Always inspect manually.
Performance Optimization
Always benchmark.
Bug Fixes
Always reproduce and validate.
Practical Advice
If you use AI in development:
Treat AI like a junior developer
Review everything.
Read generated code line by line
If you cannot explain it, do not ship it.
Keep requirements visible
Many AI mistakes come from drifting away from the original specification.
Test continuously
Never assume generated code works because it looks reasonable.
Debug before regenerating
Understanding the bug is often faster than generating another solution.
Final Thoughts
AI is transforming software development.
But there is a difference between:
"AI helped me write code"
and
"AI built my application."
One is a productivity multiplier.
The other is a gamble.
The strongest developers will not be those who reject AI.
They will be those who understand when to trust it, when to challenge it, and when to ignore it completely.
AI can write code.
Engineers are still responsible for making sure that code is correct.
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