by Amit Shekhar · 7 April 2026
Decoding Transformer Architecture
The Transformer architecture by decoding it piece by piece - understanding what each component does, how they work together, and why this architecture powers every modern Large Language Model (LLM)
Read on Outcome School ↗then come back to lock it in
Before you read, guessWhy is this mechanism necessary in transformers?
Ten seconds, a guess, then read — a wrong guess still makes the answer stick.
What this article covers
- The Big Picture
- Why Was the Transformer Needed?
- The Architecture Has Two Halves
- Decoding Step 1: Turning Words into Numbers
- Decoding Step 2: Adding Word Order
- Decoding Step 3: The Attention Mechanism
- Decoding Step 4: The Feed-Forward Network
- Decoding Step 5: Residual Connections and Layer Normalization
- Decoding Step 6: Stacking Multiple Layers
- Decoding Step 7: The Encoder in Detail
- Decoding Step 8: The Decoder in Detail
- Decoding Step 9: The Complete Flow
- The Three Variants
- Why the Transformer is So Powerful
The article lives on outcomeschool.com. Read it there, then come back: the tutor in the margin has read it and will answer questions, and the questions below check what stayed.