Modern AI Engineering

Lesson 4.6 · 30 min · video lesson

The Transformer Architecture: Built on Attention

Every major chatbot, image captioner and code assistant is built from the same few repeated parts. What are they, and how does a sentence flow through them?

In short: The Transformer turns tokens into vectors, adds position information, and then refines those vectors through a stack of identical layers. Each layer has two parts: multi-head attention, where tokens exchange information, and a feed-forward network, which processes each token on its own, both wrapped in residual connections and layer normalization. The original design had an encoder that reads the input and a decoder that writes the output; today we also use encoder-only and decoder-only versions.

What you will learn

  • Why the Transformer was needed
  • The two halves of the architecture
  • Tokenization, Embedding, and Positional Encoding
  • The Attention Mechanism and Multi-Head Attention
  • Feed-Forward Networks, Residual Connections, and Layer Normalization
  • How the Encoder and Decoder work
  • How data flows through the entire architecture
  • The three variants of the Transformer
  • Why the Transformer is so powerful

In this lesson

  1. Why the Transformer was needed
  2. The two halves of the architecture
  3. Tokenization, Embedding, and Positional Encoding
  4. The Attention Mechanism and Multi-Head Attention
  5. Feed-Forward Networks, Residual Connections, and Layer Normalization
  6. How the Encoder and Decoder work
  7. How data flows through the entire architecture (code)
  8. The three variants of the Transformer
  9. Why the Transformer is so powerful
  10. Worked example, step by step
  11. Practice: try it yourself

Key terms in this lesson

Encoder
The stack that reads the whole input and produces context-rich vectors for each input token.
Decoder
The stack that generates output tokens one at a time, using masked self-attention (and cross-attention in encoder–decoder models).
Positional encoding
Vectors added to token embeddings so the model can tell token order.
Multi-head attention
Several attention operations in parallel, each learning a different pattern, then combined.
Feed-forward network (FFN)
A small two-layer network applied to each token independently inside every layer.
Residual connection
Adding a sub-layer's input to its output: x + Sublayer(x).
Layer normalization
Rescaling each token vector to a standard mean and variance to keep training stable.

This lesson is part of the Generative AI Engineering and Complete AI Engineer tracks. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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