Transformer Architecture Basics

Transformer Architecture Basics

In 2017, a paper titled Attention Is All You Need changed the course of AI history. It introduced the Transformer architecture, which is the foundation of virtually every modern language model, including GPT, Claude, Llama, and Gemini. Before Transformers, AI had a hard time understanding context and relationships in text. After Transformers, everything changed.

The key innovation of the Transformer is the attention mechanism. Imagine reading a sentence: The cat sat on the mat because it was tired. What does it refer to? The cat, obviously. A human knows this instantly, but for a computer, this was a very hard problem. Attention allows the model to look at every other word in the sentence and decide which ones are relevant to understanding each specific word.

Previous architectures like RNNs and LSTMs processed text one word at a time, in order. This was slow and made it hard to remember things that were said earlier in a long sentence. Transformers process all the words at once, in parallel. This makes them much faster to train and much better at capturing long range relationships in text.

A Transformer is built from two main parts: an encoder and a decoder. The encoder reads the input text and creates a rich representation of its meaning. The decoder uses that representation to generate output text, one word at a time. Some models use both parts, like BERT, while others use only the decoder, like GPT. The decoder only approach is what powers most modern chatbots.

The Transformer also introduced the concept of positional encoding. Since it processes all words at once, it needs a way to know the order of words. Positional encoding adds information about each word's position in the sequence, so the model knows that cat comes before mat. This simple but clever trick, combined with attention, is what makes Transformers so powerful.