Foundations
What Is a Large Language Model (LLM)?
The term behind every AI chatbot, explained: what makes a language model "large," and what that size actually buys you.
The core idea
An LLM is a neural network trained to predict the next piece of text given everything before it. Trained on a large enough and varied enough body of text, that single prediction task turns out to be enough to produce something that can hold a conversation, write code, or summarize a document — none of which it was explicitly programmed to do.
Why "large" matters
"Large" refers to two things at once: the size of the training data (often trillions of words) and the number of parameters — the internal values the model adjusts during training (see What Are Parameters?). Both scaling up together is what turned next-word prediction from a parlor trick into something genuinely useful.
What it isn't
An LLM has no database of facts it looks things up in by default, and no persistent memory between separate conversations unless a product explicitly adds that on top. Everything it "knows" is encoded in its parameters from training — which is also the root cause of both its strengths and its failure modes like hallucination (see Why AI Models Hallucinate).
Frequently Asked Questions
What does "LLM" stand for?
Large Language Model — a neural network trained on huge amounts of text to predict the next piece of text, which turns out to generalize into conversation, writing, and reasoning ability.
Is every AI chatbot powered by an LLM?
Most modern text-based AI assistants (ChatGPT, Claude, Gemini, and similar) are built on top of an LLM, with additional training stages layered on to make the base model helpful and safe to talk to.