How Transformer Models Actually Work

If you’ve been hearing about GPT, LLMs, or AI models everywhere and wondering “what’s actually happening under the hood?” — this article is for you. Let’s break down transformer models in the simplest way possible, without heavy math or jargon. 🚀 The Big Idea A transformer model is a type of neural network designed to understand and generate language by looking at relationships between words in a sentence — all at once. Unlike older models that read text word by word, transformers read the entire sentence simultaneously. 👉 That’s the core superpower. 🧠 Step 1: Turning Words into Numbers (Embeddings) Computers don’t understand words — they understand numbers. So the first step is: Convert each word into a vector (a list of numbers) Example: "I love AI" ↓ [I] → [0.2, 0.8, ...] [love] → [0.9, 0.1, ...] [AI] → [0.7, 0.6, ...] These vectors capture meaning: "king" and "queen" will have similar vectors "cat" and "car" will be very different 🔍 Step 2: Understanding Context with Attention