Before 2017, most language AI systems relied on Recurrent Neural Networks (RNNs) and LSTMs. They worked, but they had a fundamental limitation: they processed information sequentially. Every token depended on the previous token's hidden state, making training slow, difficult to parallelize, and increasingly ineffective for long contexts. Then came a research paper from Google: Attention Is All You Need (Vaswani et al., 2017) The paper introduced the Transformer , an architect...