Transformers changed AI because they stopped reading sequences one token at a time. Instead of...
Transformers compare tokens in parallel via self-attention instead of sequentially, enabling GPU scaling and better long-range context handling than RNNs. This foundation powers modern LLMs (GPT, Claude); understanding attention efficiency and inference caching is key for production deployment ROI and cost control.
Transformers changed AI because they stopped reading sequences one token at a time.
Instead of moving step by step like an RNN, a Transformer compares tokens directly.
That one design shift made modern LLMs possible.
Core Idea
A Transformer is a neural network architecture built around attention.
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