Large language model (LLM)
Also called LLM, language model, large language models
An AI model trained on huge amounts of text to predict and generate language, like the models behind ChatGPT and Claude.
A large language model is a neural network trained on enormous amounts of text to predict the next token in a sequence. Repeated billions of times, that simple task forces the model to absorb grammar, facts, writing styles, reasoning patterns and the structure of code. "Large" refers to both the training data, often trillions of tokens, and the number of parameters, from a few billion to well over a trillion.
After this pre-training, models are usually instruction-tuned and preference-trained so they work as assistants. The models behind ChatGPT, Claude, Gemini, Llama, Mistral and many others are all LLMs, mostly built on the transformer architecture. Many now also handle images, audio or video, which makes them multimodal.
An LLM does not look facts up by default. It generates text based on patterns learned during training, which is why it can write fluently about almost anything and also why it can hallucinate. Products wrap LLMs with tools such as search, code execution and file access to make them more accurate and useful.
Example
When you ask an LLM "What is the capital of Australia?", it does not search a database. It generates "Canberra" because, across its training data, that is overwhelmingly the most likely continuation of such a question.
Common mistake
Treating an LLM like a search engine or a calculator. Without tools, it may get recent events, exact figures and long arithmetic wrong while sounding certain. Use search-enabled modes for current facts and code execution for precise calculations.