A Large Language Model (LLM) is a specialized branch of Natural Language Processing (NLP) that creates computer programs designed to imitate human intelligence, characterized by four technical pillars: neural networks, tokens, context windows, and scaling. The structural foundation is an artificial neural network, a computational model using artificial neurons to process information via weights and optimization targets. At the University of Central Florida, research extends to the Department of Physics, where faculty apply quantum computing methods to improve how neural networks prepare and represent data. LLMs utilize units called “tokens,” which are basic units of meaning representing word fragments or character sequences, converted into numbers for mathematical processing. The context window refers to the model’s “memory” during a specific interaction session, which is finite; if a conversation becomes too long, the model may yield inconsistent outputs. Scaling refers to increasing training data and computational power, requiring massive clusters of Graphics Processing Units (GPUs) and significant electricity and water. Modern generative AI systems rely on the transformer architecture, proposed in 2017, which uses attention mechanisms to help the model determine which parts of the input are most relevant.