Understanding Transformers (Part 4): Attention is just a few matrices

How simple matrix operations unlock context, meaning, and the magic behind modern AI.

This week, I am picking up where we left off: moving beyond word embeddings and positional encodings, and stepping directly into the core magic of the Transformer: self-attention.

But before we can even talk about multi-heads or decoder stacks, we need to answer one crucial question:

➡️ How does a Transformer actually mix information across an entire sentence — all at once?

In this post, we will break down the full flow of the Scaled Dot-Product Attention mechanism:

• How words are compared through queries and keys,
• Why scaling and softmax are critical steps,
• And how new, context-rich representations are built by blending Value vectors.

All of this comes down to just a few simple matrix multiplications.

Blog series

• ✅ Understanding Transformers (Part 1): Why RNNs are nearly impossible to train
• ✅ Understanding Transformers (Part 2): How LSTMs fixed Recurrent Networks (mostly)
• ✅ Understanding Transformers (Part 3): Positional encodings and word embeddings
• 📌 Understanding Transformers (Part 4): Attention is just a few matrices (this blog post!)