Attention Is All You Need — Full Paper Breakdown

The 2017 paper "Attention Is All You Need" by Vaswani et al. introduced the Transformer — the architecture behind GPT, Claude, Gemini, and every major LLM today. It replaced recurrent models entirely with attention mechanisms, and the field has never looked back. This post walks through the key ideas. The problem with RNNs Before Transformers, sequence modeling meant RNNs and LSTMs. These process tokens one at a time, left to right. That sequential dependency creates two problems: No parallelization — each step depends on the previous hidden state, so you can't process tokens simultaneously during training Long-range dependencies decay — by the time an RNN reaches token 500, the signal from token 1 has been compressed through hundreds of hidden states Attention mechanisms existed before this paper (Bahdanau attention, 2014), but they were bolted onto RNNs. The radical idea here: what if attention is all you need? Drop the recurrence entirely. The Encoder-Decoder architecture The Transf