The mechanism that revolutionized AI and powers modern language models
When you read "The cat sleeps on the mat because it is tired," how do you know "it" refers to the cat and not the mat?
Your brain instantly connects related words across the sentence. Attention is the mathematical mechanism that lets LLMs do the same thing.
Attention is arguably the most important innovation in modern AI. This chapter will build your understanding from the ground up:
Why can't models just process words independently?
How do we measure which words should pay attention to each other?
The formula that changed AI
How every word learns what's relevant from every other word
Why having multiple attention "heads" is better than one
How attention fits into the full transformer architecture
See what models actually pay attention to
The 2017 paper "Attention Is All You Need" introduced the Transformer architecture. Every major LLM since then — GPT, BERT, Claude, LLaMA, Gemini — uses attention as its core mechanism. Understanding attention is understanding modern AI.
By the time you finish this chapter, you'll understand how attention combines with the concepts you've already learned:
Q, K, V are matrices. Attention uses matrix multiplication (QKT)
Y = XW + b processes features. Attention determines WHICH features to use
Words start as embeddings. Attention creates contextual embeddings
Softmax is a non-linear function that normalizes attention scores
This chapter is being carefully crafted to make attention mechanisms as intuitive as possible. We'll use visual examples, interactive demonstrations, and clear analogies to demystify the mechanism that powers modern AI.