Large Language Models Explained
How large language models like GPT-4 and Claude actually work, what they can and cannot do, and why understanding the technology matters for evaluating AI-generated content.
What Large Language Models Actually Do
A large language model is a neural network trained on vast amounts of text to predict the next word in a sequence. GPT-4, Claude, Gemini, and Llama are all LLMs. They do not understand the world the way humans do. They have learned statistical patterns in language so deeply that they can generate text that reads as coherent, persuasive, and authoritative. This is both their power and their danger.
Training happens in two phases. First, the model reads billions of web pages, books, and articles, learning patterns like 'The capital of France is' almost always ends with 'Paris.' Second, human feedback fine-tunes the model to be helpful, harmless, and honest. But even after fine-tuning, the model's core mechanism is pattern completion, not truth verification. It will confidently generate text that sounds right even when the underlying facts are wrong.
