AI-generated code has changed how quickly I can start a project.
Recently, I used AI while building an Instagram automation bot.
I asked AI to generate the script and function calls.
It worked.
That sounds like the end of the story.
It wasn't.
The First Version Worked
The AI-generated implementation handled the basic workflow.
Functions were being called.
The automation was executing.
The prototype was useful enough to continue building.
But I noticed problems when looking beyond basic execution.
The main areas were API handling, performance, and small implementation mistakes.
There were also some typos.
None of these completely broke the application.
That's exactly why they were interesting.
Working Doesn't Mean Production-Ready
This is probably the biggest lesson I took from the project.
There are several stages between:
"It works"
and
"We should ship this"
For production, I need to think about things like: API calls
Error handling
Performance
Maintainability
User experience
Edge cases
AI can help with all of these.
But it doesn't mean the generated answer should automatically be trusted.
What I Changed
I didn't throw away the generated implementation.
That would have defeated one of the biggest benefits of using AI.
Instead, I reviewed the areas that mattered.
I changed the API handler.
I fixed typo and implementation errors.
I looked at how the functions were being executed and where the implementation could be improved.
The result was better.
But there was another important realization.
The execution was good, but the overall effect was less than I expected.
That difference is important.
Vibe Coding Is a Starting Point
My current mental model is:
Idea
↓
AI generation
↓
Working prototype
↓
Human review
↓
Refactoring
↓
Testing
↓
Production
Vibe Coding makes the first part much faster.
It doesn't remove the rest.
And as AI gets better at generating code, the review and decision-making stages become even more important.
Why This Matters for Design Engineers
This is where I think the Design Engineer role gets interesting.
A Design Engineer isn't only thinking about whether code works.
They're thinking about whether the implementation makes sense from multiple perspectives.
Does the API flow make sense?
Is the interaction clear?
Is the UI handling failure states?
Is the component maintainable?
Is the implementation unnecessarily expensive?
Does the experience match the user's actual needs?
These questions sit between design and engineering.
AI can generate the implementation.
The Design Engineer needs to shape it.
The Skill Is Changing
I don't think the future of development is simply:
AI writes everything.
It's closer to:
AI writes more of the first version, while developers spend more time reviewing, directing, testing, and improving it.
That means understanding code becomes even more important.
Not because you'll manually write every line.
Because you'll need to recognize when generated code is good, when it's questionable, and when it should never reach production.
That's the difference between generating code and engineering software.
Key Takeaways
• AI is excellent for getting from idea to prototype quickly.
• Working code still needs engineering review.
• API handling and performance can make a big difference even when the basic implementation works.
• The value of developers is shifting toward judgment, architecture, and product thinking.
• Design Engineers can become the bridge between AI-generated implementation and production-quality experiences.