Embarking on the GenAI Journey: Day 01 – Demystifying the Magic
My GenAI cohort kicked off today, and what a first day! Right away we were reminded that AI isn’t magic — at its core, models like Generative Pretrained Transformers (GPT) are doing one simple thing: predicting the next token.
I jumped straight into a hands-on assignment and built a Custom Tokenizer visualization website from scratch.
The project helped me internalize how text becomes numbers — the same idea big models rely on. Here’s a simplified look at how a query might be tokenized in this demo:
Query: Hello, How are you?
Tokenization (simplified):
Hello -> 543
How -> 3745
Are -> 867
you -> 1290
? -> 44
Conceptual lessons from Day 1
Beyond the practical build, Day 1 focused on several conceptual takeaways that make the AI learning curve much less intimidating.
You don’t need to be a math genius to work in AI. There are many meaningful roles beyond cutting-edge research.
Understanding the ecosystem and where you fit makes it easier to choose what to learn next.
We sketched a simple industry split to help visualize career paths:
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🔬 Machine Learning (The Researchers)
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• Focus on Research
• They create the core models
• They code the neural networks
• Heavy focus on Stats & Maths
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💻 Developers (The Builders)
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• They solve real-world problems
• They make money
• They build and deploy things
• Focus on Agentic AI & Workflows
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For me, knowing I’m on the “builders” side is freeing — I don’t need to rebuild models, I need to build solutions that use them.
Vector embeddings — a quick mental model
We also learned about vector embeddings: a way to map concepts into a geometric space so related things are close together. Imagine an X/Y plot where “France” is near “Eiffel Tower” and “India” is near “India Gate.” This geometric intuition is powerful for search, recommendations, clustering, and more.
What’s next
On my docket for upcoming posts:
Explain GPT to a 5-year-old
Explain vector embeddings to my mom
Explain tokenization to a fresher
I’ll be documenting each of these as simple, shareable explainers. Follow along as I convert these lessons into approachable posts and small demos.
Deep diving into a 35hrs+ course content and many other lessons.
Thanks for reading — I’ll keep sharing progress. If you’re learning GenAI too, I’d love to hear how you started and what helped you most.
