Self-attention is the core idea behind Transformer models, yet it is often explained as a black box. In this article, we build self-attention from first principles—starting with simple word interactions, moving through dot products and softmax, and finally introducing query, key, and value vectors with learnable parameters. The goal is to develop a clear, intuitive, and mathematically grounded understanding of how contextual embeddings are generated in Transformers.

Discover the fascinating journey of Artificial Intelligence from simple Sequence-to-Sequence tasks to the rise of Large Language Models. This guide traces the evolution from Recurrent Neural Networks (RNNs) and the Encoder-Decoder architecture to the revolutionary Attention Mechanism, Transformers, and the era of Transfer Learning that gave birth to BERT and GPT.