What Happens Inside a Single Transformer Layer?

The four operations inside every transformer layer

[0:00]Every Transformer layer does the same four things in the same order, and stacking enough of them gives you every modern language model. I'm Anjana. Here's what one token actually sees on its way through one layer. A Transformer model can have dozens of layers, but every layer does

Step 1: Self-attention and Query-Key-Value vector scoring

[0:16]The same four things in the same order. Self-attention, feed-forward, layer norm, residual connection. Here is what one token sees as it passes through one layer. Step one, self-attention. The

Step 2: Feed Forward Networks and non-linear feature processing

[0:30]Token revenue needs to understand its role in the sentence. It creates three vectors from itself, a query, a key, and a value. The query asks, "What should I pay attention to?" Every other token in the sentence also has a key. The query compares against every key and computes a score. High score means high relevance. Revenue scores high against

Step 3 & 4: Residual connections and layer normalization for stable stacking

[0:50]Growth and moderate because they directly affect its meaning. It scores low against we and to because they add no context. The scores become weights. The weighted sum of all values produces a new context-aware representation of revenue. The word has not changed, but now it knows what surrounds it. Step two, the

Stacking layers (BERT vs GPT) and hands-on Claude prompt

[1:11]Feed-forward network. Attention told revenue what to look at. Now the feed-forward network decides what to do with that information. It is two linear transformations with a non-linearity in between. The vector expands to a higher dimension, passes through an activation function that introduces non-linearity, then compresses back down. This is where the model transforms raw attention patterns into useful features. Attention gathers context. The feed-forward network processes it. Two more operations hold everything together. Residual connections. Before attention and before the feed-forward step, the original input was saved. After each operation, the original is added back to the result. This means the model can never completely forget what the token started as. If the layer learns nothing useful, the residual ensures the token passes through unchanged. No damage done. Layer normalization runs after

Each addition. It rescales the values so they stay in a stable range. Without it, values drift higher and higher through 12 layers and the model breaks. Residual connections protect the signal. Layer norm keeps the numbers in check. Together they are why you can stack 12, 24, or 96 layers and the model still trains. Four operations. Attention gathers context. Feed forward processes it. Residual connections protect the signal. Layer norm keeps it stable. Stack 12 of these and you have BERT. Stack 96 and you have GPT. Same four steps repeated. That is a transformer. Let's recap with Claude. Attention is the only step where a token looks at the rest of the sentence. It gathers context and it does it with a query against every other token's key. The feed forward

Network never looks sideways. It processes what attention gathered. The residual connection means a layer that learns nothing does no harm and layer norm keeps the numbers from drifting as you stack. Four operations. That is the entire building block. Your turn. Take the sentence we expect revenue growth to moderate and walk me through what self attention would compute for the token moderate instead of revenue. Which other words should it score highest against and why? Then tell me honestly, if I stacked 12 of these layers, what does the 12th layer know about moderate that the first one didn't and how would I even check? Paste that into Claude and find out whether you can predict what a layer will pay attention to before it does. Attention, feed forward, residual, norm. That's Enjana.