Transformer Architecture - Attention Mechanism Explained

The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need", is the foundation of all modern Large Language Models including GPT, BERT, and Gemini. Understanding how Transformers work helps you write better prompts, debug model behavior, and choose the right model for your use case.

The core innovation is the self-attention mechanism, which lets the model weigh the importance of every word relative to every other word in the input. Unlike RNNs, Transformers process all tokens in parallel, making them faster and better at capturing long-range dependencies in text.

The below example shows how scaled dot-product attention scores are computed for a short sentence, which is the core operation inside every Transformer layer.

It gives the following output,

Attention weights (each row sums to 1.0):
ShopMax: [0.312, 0.201, 0.294, 0.193]
sells: [0.248, 0.308, 0.219, 0.225]
premium: [0.181, 0.261, 0.341, 0.217]
laptops: [0.207, 0.184, 0.291, 0.318]
Output shape: (4, 8)

Each row shows how much attention token i gives to every other token. The model learns these weights during pre-training. Multi-head attention stacks several attention operations in parallel with different weight matrices, allowing the model to capture different relationship types at once. Positional encodings are added to embeddings before the first layer so the model understands the order of tokens in the input sequence.