How I Learn Any Technology in 3 Days Using AI Tools
Here’s The Proven Workflow to Learn Any Technology 10x Faster: AI + You
Let’s say I know nothing about Terraform. Now imagine someone challenges me to build a very complex infrastructure with lots of configurations and restrictions using it.
Honestly, I’d be confident enough to say, “Give me 2–3 days, and I’ll get it done.” Why? Because I’ve already done this in one of my previous projects.
But here’s the truth — without AI tools like ChatGPT or Claude, I wouldn’t stand a chance. I’d still be a beginner, slowly figuring things out. It would probably take me 2–3 weeks, with lots of trial and error, reading documentation again and again, and struggling to build the code.
So, if you’re curious about how I use AI tools like ChatGPT or Claude to quickly learn and build almost any idea with any technology, keep reading. That’s exactly what I’m going to share with you.
💡Quick note before we dive in: If you’d like a ready-to-use system that shows you exactly how to apply this AI-powered learning approach step by step, I’ve put it together here: Productivity Toolkit. It’s built for developers and tech learners who want to cut weeks of trial and error into just days.
You can also check out my full article here for a deeper dive: How I 10x’d My Value at Work Using AI (Without Burning Out)
Step 1: High Level Understanding — BORING BUT NECESSARY!
This is usually the easiest yet most boring part of learning a new technology. Whenever I start with something new, the first thing I do is search YouTube for the best crash course. I don’t just pick any video — I go through the comments first to see what people are saying and pre-evaluate it.
I avoid those super long tutorials that go on for 7–15 hours. Instead, I look for something short and focused — ideally 1–2 hours long, or maybe up to 5 hours if the technology is a bit more complex. At this stage, my only goal is to get a high-level understanding. I just want to grasp the basics:
The syntax
The flow of execution
The core concepts (in simple terms)
Here’s what I recommend you do at this stage:
Spend 1–2 hours max on a YouTube crash course, a blog, or even an AI explainer.
Use AI tools like Perplexity to clear up the concepts you didn’t fully understand in the video.
Remember, the goal here is simple: learn what the technology is, why it exists, and picture at least one real-world use case in your mind. That’s it.
Step 2: Understand the Problem You’re About to Solve
Now that you’ve got a basic idea of the technology, it’s time to get your hands dirty. In my experience, this is the best way to learn any technology quickly and effectively.
Think about it — you probably started learning this new tech because you already have a problem to solve, right? That’s your starting point.
Here’s what I usually do: I grab a small piece of paper (or a digital note) and write down four things about the use case I’m working on:
Context — What problem am I solving?
Goal — What’s the desired outcome?
Constraints — What limits do I have (timeline, company rules, tools, environment)?
Tech Stack — How does this new technology fit into my personal or work ecosystem?
You might be asking, “Why bother doing this?” The reason is simple: we want to end up with a 3–5 sentence project statement. This short statement explains the problem clearly and concisely. It will also help us write much better prompts when we use AI tools later in the learning and implementation process.
That’s all you need from this step — a clear project statement of just 3–5 sentences. It only takes about 5 minutes, but trust me, it makes a huge difference.
Step 3: Start the Implementation — Getting Hands Dirty
Now we’re ready to move from planning into action. This is where we start solving the use case with the help of AI tools. I usually switch between ChatGPT and Claude, depending on which one gives the better response.
1. Ask AI for the Project Blueprint
The first thing I do is ask the AI to act like a consultant and provide me with a high-level architecture. Specifically, I ask for:
File/folder structure
Dependencies needed
Integrations required
I/O mechanisms (how data flows in and out)
Why? Because this blueprint is my map. It guides me while exploring new territory.
👉 Tiny Prompt I Use
(Paste your project statement in place of <project> = <context>+<goal>+<constraints>+<tech stack>)
Act as a senior consultant. Given this project: <project>, generate a high-level architecture, file/folder layout, required dependencies, integrations, and I/O details.
2. Personalize the Plan
I don’t just accept the first map the AI gives me. I always:
Read through the plan to understand the big picture.
Cross-check doubtful parts with official documentation.
Add my own ideas, constraints, or preferences.
The goal is to make the plan yours — not just something copied from AI.
3. Break the Plan into Sub-Goals
Once I have the plan, I break it into smaller sub-goals. Each sub-goal is like a building block of the final solution.
Here’s how I approach it:
Split the problem into multiple sub-goals.
For each sub-goal, ask the AI for a step-by-step implementation plan.
Implement → Test → Validate.
If stuck, ask the AI to debug with you.
4. Learn While You Implement
This part is very important: don’t just copy-paste AI’s code.
Instead, do this:
Compare the response with what you already know.
If you’re unsure about a step, ask the AI: “What does this mean? Why is it done this way?”
That way, every sub-goal is not just implementation, but also active learning.
5. Assemble and Integrate
Each sub-goal should produce a working, tested component. Once all pieces are ready, we put them together to form the complete solution.
While assembling, I often ask the AI:
What are common pitfalls in integration?
What’s the best way to connect these components?
This helps avoid mistakes and ensures everything works smoothly.
6. The Outcome
After a few rounds of building, debugging, and refining, you’ll end up with a working solution that solves your original problem.
I’ve personally used this method in my career. Even with just 1.7 years of corporate experience, I was recognized by stakeholders for my technical expertise, timely delivery, and high-value contributions.
And that’s the real power of this approach:
👉 AI + You = A powerful combo.
With AI as your partner, you can confidently tackle any project — whether it’s personal or professional.
Example Prompt:
I want to learn [technology] by building a project. My context is [X], my goal is [Y], and my constraints are [Z].
1. Give me a high-level architecture (file structure, dependencies, integrations).
2. Suggest a step-by-step roadmap for building it.
3. Break it down into sub-problems I can tackle one at a time.
4. For each sub-problem, provide a small working example.
Step 4: Final Learning Phase — Deepening Understanding!
By now, you’ll have a working solution that actually solves your problem. That’s already a big win. But here’s the thing: if you stop here, you’ll only know how to make it work once. You won’t truly understand the technology at a deeper level.
That’s where the Final Learning Phase comes in.
Here’s exactly what I do at this stage:
1. Feedback Loop
I look back at what worked, what didn’t, and where I got stuck. Then I ask AI to explain the “why” behind those tricky parts.
Example: “This config finally worked after three retries. Can you explain why the earlier approaches failed?”
This closes the loop and turns mistakes into permanent lessons.
2. Validate Against Reality
AI is powerful, but it can be wrong. To make sure my knowledge is bulletproof, I:
Skim the official documentation for confirmation.
Check GitHub issues or community forums to see how real engineers solved similar problems.
Compare my approach with best practices suggested by the community.
This step ensures you’re not just learning shortcuts from AI, but building a solid foundation.
3. Lock It In by Teaching
The best way to master a technology is to teach it. I usually:
Write a short blog post or LinkedIn note about what I built.
Create a GitHub repo with comments + a clear README.
Even explain my solution back to AI, asking it to challenge me with questions.
Teaching forces clarity. If you can explain it simply, you truly understand it.
That’s how you transform AI from just being your “coding intern” into your personal mentor. You don’t just build solutions — you also grow into the kind of engineer who can solve problems even without AI.
Let’s Work Together!
If you’re building automation-heavy systems or looking to integrate AI agents into your Application or DevOps or FinOps workflows — I’m open to freelance gigs, consulting, and full-time roles (remote-friendly). Whether you’re a startup needing rapid prototypes or an enterprise looking to scale automation, let’s connect!
📧 Reach out on LinkedIn or shoot me a DM — let’s build something great.
Connect: LinkedIn | Gumroad Shop | Medium | GitHub