Large Language Models (LLMs) function as sophisticated machine learning systems that generate human-like text by iteratively predicting the most probable next token in a sequence. The process begins with a language processing pipeline where input text undergoes tokenization, breaking strings into smaller units, which are then converted into high-dimensional numerical vectors called embeddings to capture semantic relationships. The core architecture driving this capability is the Transformer, which utilizes a self-attention mechanism to weigh the relevance of different tokens in a sequence using Query (Q), Key (K), and Value (V) vectors. This is enhanced by multi-head attention for diverse pattern recognition, positional encoding to maintain word order, and stacked feed-forward networks for deep feature processing. During training, models adjust billions of parameters via backpropagation to minimize loss in a next-token prediction objective. During the inference phase, the model generates logits that are transformed into probabilities via a softmax function. Users can influence output randomness through a temperature parameter, where lower values (e.g., 0.1) increase determinism and higher values (e.g., 1.0) increase creativity. The model's effective "memory" is constrained by its context window, which defines the maximum token capacity for processing simultaneous input and generated output.