A large language model, or LLM, is a type of AI system trained on enormous amounts of text to predict what word or phrase is likely to come next in a sequence. That simple objective, guessing the next piece of text, turns out to be powerful enough to produce systems that can answer questions, summarise documents, write code, and hold conversations.
How LLMs Are Trained
Training happens in stages. First, the model is exposed to a vast collection of text from books, articles, and web pages, learning general patterns of grammar, facts, and reasoning by repeatedly predicting missing or upcoming words. Afterward, many LLMs go through a fine-tuning stage where human feedback is used to make the model's responses more helpful, accurate, and aligned with what users actually want.
Why 'Large' Matters
The word 'large' refers to the number of internal parameters, the adjustable numeric values the model uses to make predictions, which can run into the hundreds of billions. Larger models trained on more data generally show improved reasoning and broader knowledge, though size alone does not guarantee accuracy, and even very large models can produce confident but incorrect answers.
LLMs are now embedded in search engines, office software, coding tools, and customer service systems. Because they generate text based on statistical patterns rather than true understanding, it is still good practice to verify important facts from a reliable source rather than trusting an LLM's output blindly.