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Should Junior Developers Use AI for Coding? The Honest Answer

Cowrie
Cowrie
Dev @ Bswen

I stared at my screen, stuck on a React component for the third hour. The error message made no sense. I’d been coding for six months, and I still couldn’t build a simple todo list without help.

“Should I just ask ChatGPT to write it for me?”

That question haunted me. I knew AI could solve my problem in seconds. But I also remembered learning to ride a bike—you don’t get better by watching someone else ride. So I kept struggling.

That struggle turned out to be the right choice. But not for the reasons I thought.

The Real Problem: When Help Becomes a Wall

Here’s what happens when you lean too hard on AI as a beginner:

The trap cycle
1. Get stuck on a coding problem
2. Ask AI for solution
3. Copy-paste code that works
4. Move on without understanding
5. Hit similar problem later
6. Ask AI again (repeat forever)

I call this the “AI dependency loop.” Each iteration feels productive. You ship features! Your code runs! But you’re not building the neural pathways that make you a real developer.

A developer on Reddit put it bluntly: “I suspect that you never truly learned JSX, and you’ve been relying so heavily on Google and AI, that it’s preventing you from actually learning and retaining React.”

That hit home. I realized my “working” projects weren’t evidence of skill—they were evidence of my ability to prompt an AI correctly.

Why Typing Matters More Than You Think

Someone in that same discussion said something that changed how I approach learning: “Programming fluently is a muscle memory skill.”

At first, I thought that was old-school thinking. Why does it matter if I type code or paste it?

Then I tried an experiment. I built the same React component three ways:

  1. Copy-pasted from AI: Took 2 minutes. Worked perfectly.
  2. Typed from AI output: Took 15 minutes. Made three typos that I caught.
  3. Typed from understanding: Took 45 minutes. Made multiple mistakes, fixed them, understood why each line existed.

Two weeks later, I tried building a similar component from memory.

  • The copy-paste version? I couldn’t remember anything.
  • The typed-from-output version? I remembered the structure but got lost on details.
  • The typed-from-understanding version? I could reproduce it with variations.

The difference wasn’t intelligence. It was the struggle. When I made typos and fixed them, I learned. When I wondered “why does this hook go here?” and figured it out, that knowledge stuck.

The Stack Overflow vs AI Question

A senior developer on that Reddit thread made a point I initially rejected: “I recommend not using AI at all during your learning. Google with Stack Overflow is fine because you still need to apply some amount of thinking.”

I pushed back on this. Stack Overflow answers often just get copy-pasted too. What’s the difference?

The difference is friction. Stack Overflow forces you to:

  • Read multiple answers to find the right one
  • Evaluate which solution fits your context
  • Adapt code that doesn’t quite match
  • Read comments explaining why solutions work or fail

AI can do all this too—but only if you use it right. The problem is AI makes it too easy to skip the understanding phase.

How I Use AI Now (And How I Wish I’d Used It Earlier)

I’ve settled on what I call the “Explain-First Protocol”:

My current AI workflow
1. Ask AI: "What pattern should I use here?"
2. Request: "Explain the approach with pseudocode"
3. Ask follow-up: "Why this approach over alternatives?"
4. THEN ask for actual code WITH line-by-line comments
5. Type the code myself (no copy-paste)
6. Modify one thing to test my understanding
7. Break something intentionally and debug it

The key shift: I use AI to understand, not to generate.

Here’s a concrete example. When I was learning useEffect, I didn’t ask “write me a useEffect example.” I asked:

Better questions get better learning
"How does useEffect's dependency array actually work?
I keep hearing 'it runs when dependencies change'
but I don't understand what 'change' means for objects."


Then: "Can you show me the exact comparison React does?
Like, if I pass the same object reference vs a new object
with identical values?"


Then: "Why did React choose this behavior? What problems
does it solve?"

Each question built on the previous. By the end, I understood not just how useEffect works, but why the React team designed it that way. That understanding transfers to other hooks, other frameworks, other problems.

What Senior Developers Actually Do With AI

Here’s something that surprised me: senior developers use AI heavily—but differently.

One comment stood out: “I haven’t written code by hand in 9 months but I’m furious when Claude writes shit code.”

Read that again. This developer uses AI constantly. But they have the judgment to know when AI output is good or bad. They can evaluate, debug, and improve what AI produces.

That’s the goal. Not avoiding AI, but developing the expertise to use it as a tool, not a crutch.

Another insight: “The devs who get the most out of it aren’t the ones who know the most syntax—they’re the ones who know how to ask the right questions.”

This reframed everything for me. The skill isn’t memorizing syntax. The skill is:

  • Formulating precise questions
  • Understanding the answer’s implications
  • Spotting when an answer is wrong or incomplete
  • Knowing how to verify and test

The Test I Use to Check My Learning

Whenever I think I’ve learned something using AI, I apply this test:

The reproduction test
Can I explain this concept to someone else?
Can I rebuild this from scratch in 30 minutes?
Can I identify what would break this code?
Can I adapt this to a slightly different scenario?

If I answer “no” to any of these, I haven’t learned it—I’ve just collected it. Time to go back and struggle through the understanding.

One pattern I’ve noticed: beginners often skip official documentation in favor of AI explanations. This is a mistake.

The React docs at react.dev/learn are genuinely excellent. They explain not just what but why. Reading them alongside AI explanations creates a fuller picture.

I now follow this sequence:

  1. Check official docs first
  2. Use AI to clarify confusing parts
  3. Type examples from docs
  4. Ask AI for alternative explanations if still confused
  5. Build something original to test understanding

A Warning About AI’s Blind Spots

AI has a critical weakness: it confidently gives wrong answers.

I once asked an AI about React’s rendering behavior and got an answer that sounded completely correct. Every sentence made sense. The code examples worked. The explanation was clear.

A week later, I read the React documentation and realized the answer was subtly but importantly wrong. Not in a way that would break code, but in a way that would warp my mental model.

This taught me to always verify AI explanations against authoritative sources. AI is a starting point, not the final word.

The Honest Answer to the Original Question

So should junior developers use AI for coding?

Yes—but as a teacher, not a generator.

The dividing line isn’t whether you use AI. It’s whether you could explain what you “wrote” afterward. It’s whether you could reproduce it from understanding. It’s whether you’re building judgment or just building output.

I wasted months in the AI dependency loop. Don’t make my mistake. Type the code. Break the code. Debug the code. Understand the code.

AI will make you faster. But understanding will make you a developer.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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