Cracking Self-Attention: The Matrix Math That Powers Transformers

Self-attention is a matrix operation, not merely a concept—which explains why Transformers scale to massive models. This mechanism allows tokens to dynamically learn contextual representations by comparing with other tokens, forming the mathematical core of modern language models. Understanding QKV projections and softmax scaling is critical because it reveals the exact mathematical foundation that makes GPT, Claude, and other transformer-based systems work.