How to Learn New Tech in the Era of LLMs

We are in a strange phase of software development.

You can type one prompt and get:

• a component
• an API route
• a migration script
• test cases
• documentation

What used to take hours can now take minutes. That is powerful. But it creates a real learning problem. In the past, effort forced learning. You had to read docs, debug errors, and rewrite code until it clicked.

Today, AI can remove much of that friction. If you are not careful, you will ship faster but understand less. This post is about learning new tech without becoming dependent on LLM output.

The Real Risk: Speed Without Retention

LLMs can help you move very fast. But fast input often leads to weak memory if you do not process it deeply. The issue is not speed itself, the issue is skipping cognitive effort. When your brain does not wrestle with the problem, retention drops quickly.

If a solution comes too quickly:

• you may skip fundamentals
• you may not understand why it works
• you may fail when requirements change
• you may become good at prompting but weak at engineering

This is not anti-AI. It is anti-passive learning. Don't beleive me, just try to build a project without LLM and see the difference. Those who are using LLM from the begining are not able to build projects without it. I have seen many people who are not able to build projects without LLM. I am not against LLM, I am against passive learning.

LLM Is a Tool, Not a Brain Replacement

LLMs are excellent for:

• summarizing documentation
• generating initial boilerplate
• exploring alternatives quickly
• explaining unfamiliar syntax

But they are dangerous when you use them to avoid thinking. The same tool can either accelerate you or weaken you depending on when you use it. Use it as a second brain for verification, not as a first brain for every step. The goal is not “use less AI.” The goal is “use AI at the right step.”

A Practical Rule: 4:1 Learning Ratio

Use a simple discipline rule, 4:1 that means if you are doing any thing do atleast 20% by yourself, or if you are working 5 days a week then atleast 1 day should be without LLM assistance. If you do that you will see you are geting faster and stronger too, our dependency should not become our weakness, we should use it as a tool to make our life easier, not to replace our brain.

• 4 parts self-effort
• 1 part LLM assistance

That means:

1. Read docs or examples first
2. Try implementation yourself
3. Debug at least one failure yourself
4. Explain the approach in your own words
5. Then ask LLM for review, optimization, or missing edge cases

This keeps ownership with you while still benefiting from AI speed. It also creates a healthy loop: effort first, assistance second, reflection third. That loop is what converts short-term output into long-term skill.

For Students: Try “No LLM During Build”

If you are still building fundamentals, use stricter training cycles.

A strong approach:

• While building: no LLM
• After building: use LLM for review only

Why this works:

• You build problem-solving muscle
• You practice debugging from first principles
• You create stronger recall under interview pressure

If you use AI too early, you may pass tutorials but fail independent tasks. And independent tasks are what matter in interviews, internships, and real jobs.

The 100% Attempt Rule

Before asking LLM for help, do your full attempt. Think of this as a guardrail against passive consumption.
If you first struggle honestly, AI feedback becomes much more valuable.

“100% attempt” means:

• You understood the requirement
• You wrote an approach
• You tried coding it
• You hit a real blocker and can describe it clearly

Then ask targeted questions like:

• Why is this hook re-rendering infinitely?
• What edge cases am I missing in this parser?
• How can I reduce this from O(n²) to O(n log n)?

Precise questions create learning. Vague prompts create dependency. Specific prompts force you to define your gap clearly.
That act alone improves your debugging and architectural thinking.

How to Use LLM to Learn Faster (The Right Way)

Use this loop for any new technology:

1. Build a small project manually
2. Ask LLM to review architecture decisions
3. Compare your solution with AI alternatives
4. Refactor one part yourself
5. Write a short “what I learned” note

This gives you speed and retention together. You still move fast, but now you actually keep what you learn.

A Simple Prompting Framework for Learners

When you do use LLM, prompt for understanding, not just output. The quality of your prompt should reflect the quality of your thinking.
If the prompt is lazy, the learning outcome will also be lazy.

Bad:

Build this entire app for me.

Better:

I implemented this feature. Here is my code.  
Find logic bugs, performance issues, and explain why each fix matters.

Best:

Give me 3 approaches.  
Compare tradeoffs.  
Then quiz me with 5 questions to check if I understood.

Signs You Are Learning Well in the LLM Era

You are on the right track if:

• You can explain your code without opening the file
• You can rebuild core parts from memory
• You can debug when AI output fails
• You can choose between multiple approaches intentionally

If these are missing, reduce AI usage during implementation and increase self-attempt time. Treat this as a calibration checklist every few weeks, not a one-time judgment.

Final Thoughts

LLMs are one of the best accelerators developers have ever had. They can make you learn faster than ever, but only if you stay disciplined.

Without discipline, AI makes you dependent.
With discipline, AI makes you dangerous in the best way.

Use LLM after effort, not instead of effort.

That is how you become a strong developer, not just a strong prompt engineer.