How Large Language Models Like ChatGPT Work: A Plain-English Explainer

Chatbots such as ChatGPT generate human-like replies by repeatedly predicting the most likely next token, which are small chunks of text. The model learns these patterns through pre-training on vast amounts of text, storing what it learns as numerical parameters, and is then fine-tuned on helpful conversations to act more like an assistant. Most modern models use the transformer design, whose attention mechanism weighs how earlier tokens relate to the next one. Because outputs are built from plausible patterns rather than verified facts, these systems can state false information confidently, a problem known as hallucination.