What Large Language Models Actually Are and How They Work

Large language models (LLMs) are computer programs trained on trillions of words of text to predict the next word or token in a sequence, with no true understanding or consciousness involved. Models like GPT-4 are built on neural networks containing an estimated 1.7 trillion parameters, trained on roughly 13 trillion tokens sourced from books, websites, and other internet content. After initial training, LLMs undergo additional fine-tuning to follow instructions and behave as helpful chatbots while refusing harmful requests. While LLMs excel at text generation, summarization, and code assistance, they struggle with accurate math, real-time information, and can confidently produce false information — a phenomenon known as hallucination. Training such models costs between $50 million and $100 million, and ongoing inference costs mean API access for developers typically comes at a price.