# Transformer Architecture Explained: Self-Attention, Encoders & Decoders

> How the Transformer works: self-attention, positional encodings (incl. RoPE), feed-forward layers, and normalization patterns explained with diagrams and code.

Published: 2026-03-01  
Canonical: https://aimastermindlab.com/knowledge-base/transformer-architecture

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Transformers replace recurrence with attention, letting every token attend to every other. This section‑by‑section reference clarifies the moving parts.

Self‑attention: compute Q, K, V projections; use scaled dot‑product attention with softmax; combine multiple heads to capture diverse relations.

Positions: compare sinusoidal, learned, and rotary embeddings. Note how RoPE improves extrapolation and is widely used in 2025–2026 LLMs.

MLP blocks: GELU‑based two‑layer networks carry much of the model capacity; discuss gating and SwiGLU variants.

Norm/residual: pre‑norm vs post‑norm trade‑offs; stability considerations; why pre‑norm dominates modern stacks.

Training notes: scaling, token mixing, and optimizer choices (AdamW, LAMB). Include tips on gradient clipping and activation checkpointing.
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Source: https://aimastermindlab.com/knowledge-base/transformer-architecture — AI Mastermind Lab (https://aimastermindlab.com)