Transformer architecture is a deep learning model introduced in 2017 that revolutionized natural language processing (NLP) and many other AI applications. Unlike traditional neural networks, transformers process data in parallel rather than sequentially, making them much faster and more efficient. They rely on a mechanism called "self-attention," which allows the model to weigh the importance of different input elements. This architecture is the foundation for many advanced AI models like GPT (used in ChatGPT) and BERT, enabling tasks such as translation, summarization, and even design analysis in architecture.