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.