attention

A deep tour of the architectural choices powering today's large language models — decoder-only Transformers, encoder-decoder designs, grouped-query attention, RoPE positional embeddings, and mixture-of-experts routing — with concrete numbers and trade-offs.

From the limits of RNNs to the self-attention mechanism that replaced them. Learn how queries, keys, and values implement scaled dot-product attention, why multi-head attention captures richer structure, how positional encodings inject order, and how all of this assembles into a transformer block.