How Transformer Architecture Became the Backbone of Modern AI Systems

Transformers are the foundational architecture powering today's leading AI models, including large language models like ChatGPT. Earlier architectures such as Recurrent Neural Networks struggled with long sequences, losing context from earlier inputs, which motivated the development of attention-based models. The Transformer addresses these limitations by using a multi-head self-attention mechanism within stacked encoder and decoder layers — six of each in the original design. Before processing, input text is broken into tokens converted into numerical vectors, with positional encodings added using sine and cosine functions to preserve word-order information. The self-attention mechanism allows each token to weigh its relevance against all others via Query, Key, and Value projections, enabling richer contextual understanding across entire sequences.