Self-attention models lack an inherent sense of word order. This article explains positional encoding in Transformers from first principles, showing how sine–cosine functions encode absolute and relative positions efficiently and enable sequence understanding.

Transformers are the foundation of modern AI systems like ChatGPT, BERT, and Vision Transformers. This article explains what Transformers are, how self-attention works, their historical evolution, impact on NLP and generative AI, advantages, limitations, and future directions—all explained clearly from first principles.