Modern Large Language Models (LLMs), like GPT, Claude, and Gemini, rely heavily on the transformer architecture. Introduced in 2017, this architecture is a sequence prediction algorithm built from neural network layers. It converts text into a numerical format, processes it through multiple layers, and then converts the results back into text, enabling complex language understanding.

At the start, text is broken down into tokens, which are then converted into embeddings. These embeddings, vectors of numbers, capture semantic relationships between words, allowing for mathematical operations within the model. Positional embeddings are added to provide context on word order, a crucial element for the model to understand sentence structure.

The core of the transformer lies in its attention mechanism. This mechanism processes each token, comparing it against others to determine their relationships. By assigning weights to different parts of the input, the model understands context and meaning. Each layer refines the representation progressively, extracting deeper levels of meaning.

This architecture's ability to process words simultaneously allows LLMs to learn from massive datasets. The transformer's design has become the standard for modern AI models, driving advancements in natural language processing. Understanding these foundational principles is essential for anyone working with or studying AI.