A Large Language Model, or LLM, is an artificial intelligence system trained on massive amounts of text data. It learns to understand and generate human language by identifying patterns and relationships in this data. The core function of an LLM is to predict the most probable next word in a sequence, based on all the preceding text.
The first step in creating an LLM is collecting massive amounts of training data. This includes text from web pages, books, code repositories, scientific papers, and social media. The scale is enormous - often billions or even trillions of words. This diverse data allows the model to learn the rich patterns and structures of human language across many different domains and writing styles.
Before training begins, text is broken down into tokens - smaller units like words or sub-words. During training, the model reads a sequence of tokens and tries to predict what comes next. It compares its prediction with the actual next token and adjusts its internal parameters to improve accuracy. This process repeats billions of times across the entire dataset, gradually teaching the model the patterns and structures of language.
Modern LLMs primarily use the Transformer architecture, which is particularly effective for language tasks. The Transformer excels at processing sequential data and understanding long-range dependencies between words. Its key innovation is the attention mechanism, which allows the model to focus on relevant parts of the input when making predictions. This architecture enables parallel computation and helps the model understand context across entire documents, not just nearby words.
To summarize how Large Language Models work: They learn from massive text datasets to understand language patterns. Training involves predicting the next word in sequences billions of times. The Transformer architecture enables understanding of long-range context. These models generate text through statistical pattern recognition rather than true understanding. Their applications include translation, writing assistance, and question answering across many domains.