We talk constantly about the implications of AI—the ethics, the jobs, the singularity. But very few of us have a concrete mental model of the engineering that makes it happen.
We know it's not magic. We know it's math. But what does that math look like in action?
I wanted to bridge the gap between high-level hype and low-level code. So, I built a visual breakdown that traces the life of a single prompt: "Write a poem about a robot."
I followed that prompt through the entire neural pipeline—Tokenization, Embeddings, Attention, and the KV Cache—to visualize exactly how a machine "thinks."
Here is the full 16-minute visual breakdown:
🧠 The Core Concepts (Visualized)
If you don't have time for the video right now, here are the top three mechanical analogies I use to replace the abstract jargon.
- Embeddings are a "Grocery Store" How does a computer understand that "King" - "Man" + "Woman" = "Queen"? It’s not looking up definitions. It’s looking up locations.
Imagine a massive, hyper-dimensional Grocery Store.
Apples are shelved next to Bananas.
"King" is in the "Royalty" aisle.
"Robot" is in the "Technology" aisle.
When the AI sees a word, it doesn't read it; it turns it into coordinates (vectors). This allows it to understand relationships based on "distance" rather than definitions.
- Attention is a "Cocktail Party" The biggest breakthrough in Generative AI was the Attention Mechanism. But how does it work?
Think of it like a loud Cocktail Party. You act as the AI. You are surrounded by noise (tokens). Most of it is irrelevant. But if someone shouts your name, or a topic you care about, you snap to attention.
The model does this mathematically. When it processes the word "Bank", it scans the rest of the sentence (the room).
If it hears "River," it pays attention to the nature meaning of Bank.
If it hears "Money," it pays attention to the financial meaning.
- The Context Window is a "Workbench" We often worry about AI "forgetting" things in long conversations. This isn't a memory failure; it's a space failure.
The Context Window isn't an infinite brain; it’s a physical workbench. You can only fit 8,000 (or 128k) tools on the bench. Once it's full, if you add a new tool, the oldest one falls off the edge.
Why this matters
Understanding these mechanics removes the fear of the "Black Box." When you see that "Hallucinations" are just probabilistic guesses based on vector math, the AI feels less like a magic spirit and more like a powerful engine.
I also cover RLHF (Reinforcement Learning from Human Feedback) in the video, explaining how we train the "Wild Wolf" (Base Model) into a "Helpful Dog" (Instruct Model).
Let me know if these analogies help you grasp the science behind the hype! 👇
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