1. Tokenization & Positional Embeddings: Words map to continuous IDs, merged with sine/cosine geometric frequencies so sequence order is preserved.
2. QKV Linear Projections: Each token creates a Query (needs), Key (offers), and Value (content) vector.
3. Scaled Dot-Product Attention: Relevance is scored via dot product, scaled by $\sqrt{d_k}$ to prevent saturated gradients, then normalized via Softmax.
4. Residual Add & LayerNorm: $x + \text{Dropout}(\text{SubLayer}(x))$ stabilizes deep gradient backpropagation across 12–96 stacked layers.
Inspecting Token: Select token
Q Query (What am I searching for?):
[0.42, -0.19, 0.88, 0.12]
K Key (What do I advertise?):
[0.31, 0.55, -0.12, 0.76]
V Value (Information to pass forward):
[0.91, 0.04, 0.38, -0.62]
Simulated Next-Token Softmax Distribution (Context: )