How Transformers Work

How Transformers Work

Almost every large language model today — ChatGPT, Claude, Gemini, Llama — is built on an architecture called the Transformer. It was introduced in a 2017 paper titled "Attention is All You Need" and it changed AI forever.

You don't need to understand the maths to understand the key idea.

The Problem Transformers Solved

Before Transformers, language models processed text sequentially — word by word, left to right. This had a major weakness: long-range dependencies.

In the sentence "The trophy didn't fit in the bag because it was too big", what does "it" refer to — the trophy or the bag? A human knows immediately. But a model processing left to right might have "forgotten" the context of "trophy" by the time it reaches "it".

Transformers solved this with a mechanism called attention.

Attention: The Key Idea

Attention allows a model to look at all tokens in the input simultaneously and decide which ones are most relevant to each other.

When processing the word "it" in our example, an attention mechanism can look back at "trophy" and "bag" and decide — based on learned patterns — that "trophy" is more relevant.

This is done with attention scores: numerical weights that say "when processing token X, how much should I pay attention to token Y?" These scores are learned during training.

Self-attention is when a token attends to other tokens in the same sequence. Multi-head attention runs this process in parallel multiple times, letting the model capture different types of relationships simultaneously.

The Transformer Architecture

A Transformer model is a stack of layers, each containing:

1. Multi-head self-attention: tokens attend to each other
2. Feed-forward network: processes each token's representation independently
3. Layer normalisation: stabilises training
4. Residual connections: help gradients flow during training

Modern LLMs might stack 96 or more of these layers. Each layer refines the model's understanding of the input.

Why It Matters for You

Understanding Transformers explains some practical things:

• Context windows exist because attention over very long sequences is computationally expensive (it scales with the square of the sequence length)
• Models can understand context across long passages — they're not just looking at nearby words
• The same architecture works for text, images, audio, and code — Transformers are remarkably general

The 2017 paper that introduced this has been cited over 100,000 times. It's arguably the most influential AI paper ever written.