Transformer Architecture

The transformer is a neural network architecture introduced in the 2017 paper 'Attention Is All You Need' by researchers at Google. It replaced earlier sequential architectures like recurrent neural networks (RNNs) and LSTMs with a mechanism called self-attention, which allows the model to process all parts of an input simultaneously rather than one element at a time. The key innovation is the self-attention mechanism, which enables each element in a sequence to attend to every other element, learning which parts are most relevant to each other. When processing a sentence, for example, the model can directly connect a pronoun to the noun it refers to, regardless of the distance between them in the text. Transformers consist of encoder and decoder blocks (or just one of these, depending on the variant). BERT-style models use only the encoder for understanding tasks. GPT-style models use only the decoder for generation tasks. The original transformer used both for sequence-to-sequence tasks like translation. This architecture has proven extraordinarily versatile and scalable, forming the foundation of virtually all modern large language models.

The transformer architecture is the foundation of the generative AI revolution. Every major language model — GPT, Claude, Gemini, LLaMA — is built on transformers. Understanding this architecture helps business leaders grasp why current AI capabilities exist and where they are heading. Transformers' ability to process input in parallel makes them highly efficient on modern GPU hardware, enabling the training of models with hundreds of billions of parameters on massive datasets. This scalability is what has driven the rapid improvement in AI capabilities over recent years. For organisations building or customising AI systems, understanding transformers informs key technical decisions: how to structure input data, why context window limits exist, what trade-offs are involved in model size versus speed, and how fine-tuning works. This knowledge helps teams have more productive conversations with AI engineers and make better architectural choices.