A transformer is a deep learning architecture introduced in the landmark 2017 paper "Attention Is All You Need" by researchers at Google. It changed natural language processing and has since become the foundation for virtually all state-of-the-art language models, including GPT, BERT, T5, and LLaMA. Unlike earlier sequence models such as RNNs and LSTMs, transformers process all tokens in a sequence simultaneously, enabling massively parallel computation and far more effective capture of long-range dependencies.
The key innovation of the transformer is the self-attention mechanism, which allows the model to weigh the relevance of every token in a sequence relative to every other token. This enables the model to understand context regardless of the distance between related words. The architecture consists of an encoder (which processes input) and a decoder (which generates output), though many modern models use only one of these components: BERT uses only the encoder, while GPT uses only the decoder.
Transformers have proven remarkably versatile beyond NLP. They now power breakthroughs in computer vision (Vision Transformers, or ViTs), protein structure prediction (AlphaFold), code generation, music composition, and multimodal AI systems. Their ability to scale with larger datasets and more parameters has driven the trend toward increasingly powerful foundation models that can be fine-tuned for specific downstream tasks.
Understanding transformer architecture matters for any team building AI-powered products. Transformer-based models are used in custom solutions for text generation, document understanding, search relevance, chatbots, and other intelligent features that bring modern AI capabilities to real-world applications.