I have a computer science degree with a specialization in AI and Data Science. And I still didn't know what prompt engineering actually was.
Not really. Not until three weeks ago.
That's the honest starting point.
I graduated, looked at my skills, and realized that a lot of what the real world is talking about — GenAI, prompt design, modern full-stack tools — either wasn't covered in my coursework or just went past me without landing.
I could have waited until I felt ready. Instead, I decided to relearn it in public.
Who I am
Fresh engineering grad. Coimbatore-based. Interested in AI and full-stack development, and currently figuring out how to get better at both at the same time.
I'm not here to perform expertise I don't have. I'm here to build, document what I learn, and eventually land a job that lets me keep doing this.
What I'm relearning and building
Four things on my plate right now:
- AI/ML fundamentals — from the ground up, not just theory
- MERN stack — building real full-stack projects
- React → Next.js — making the transition properly
- JavaScript → TypeScript — because I keep seeing it everywhere and I need to stop avoiding it
These aren't goals I set to sound ambitious. They're the gaps I noticed when I looked at job listings and got honest with myself.
Where I am right now
Started with Elements of AI from University of Helsinki — low pressure, good foundation reset.
Then Generative AI for Beginners from Google.
Currently working through the IBM Applied AI Developer Professional Certificate. Three courses done:
- Introduction to AI ✅
- Generative AI – Introduction and Applications ✅
- Generative AI – Prompt Engineering Basics ✅
Three more to go.
The prompt engineering course is where something actually clicked. I'd been using ChatGPT and Claude for a while and thought prompts were just — type what you need, maybe fix your grammar, get an answer.
Turns out prompts have types. Paradigms. Building blocks. There are tools built specifically to optimize them. I had seen "prompt engineering" mentioned a hundred times and never actually understood what it meant.
The fact that I specialized in AI in college and still missed this says a lot about the gap between academic curricula and where the field actually is.
What comes next
Projects. Real ones.
I'll share what I build, what breaks, what I figure out, and what I wish someone had told me earlier — not just polished final results, but the messy middle too.
I'll also share dev news and tools I come across that seem worth passing on. This won't only be about my own work.
The honest goal
I want to land a job in AI or full-stack development. Not hiding that.
But I also want to keep growing after I get there. Building in public is how I plan to stay accountable — not just during the job hunt, but after it.
If you're in a similar phase — relearning, rebuilding, or just starting out — I'd genuinely like to hear from you.
What's one thing you wish your college had actually taught you about working in tech?
Drop it in the comments. Let's compare notes. 👇