LLM.
Also known as: Large Language Model
An AI-powered word predictor. An LLM is a type of AI model trained on huge amounts of text to be able to become a prediction machine for more text based on what you tell and ask it in your prompt and context. LLMs can answer questions, summarise, write code, all based on language. But it is just a predictor and an LLM is not a search engine or database and it recreates patterns it's observed without having any understanding or consciousness of the world around it. In an LLM, words are chunked down into tokens, essentially giving them coordinates in space allowing it to then work out which word is most likely to come next. The model is assembled with statistics and a neural network (a mesh of possibilities) where words and numbers become tokens with weights showing probability of being next. Usually when we refer to "model" in AI, we are actually using shorthand to refer to something bigger than just an LLM. For example, ChatGPT can be powered by a GPT-5.6 Sol model and within the 5.6 Sol model there will be an LLM but also a load of other ingredients that help the whole thing work together as one cohesive system.
In the wild
“Beijing-based Moonshot AI unveiled ‘Kimi K3’, a powerful reasoning LLM that can handle large swathes of text”
Seen here: Nature, July 2026
For AI users
LLMs are prediction machines, powered by what they're fed (their corpus and your prompts and contexts). But all they do is predict. LLMs do not "think", instead they are just predicting via a matrix of predictions. Prediction can also be all about the next most likely thing and at times creative work or problem solving doesn't want the most likely thing but something else entirely.
For business leaders
They're a prediction machine, so be mindful of the outputs but also the inputs. Prompt engineering and context engineering are so important, as is model selection to make sure the LLM chosen is up to scratch for your needs (or too big / not relevant for your needs), and understand that their corpus and training might create bias risks. As an LLM is stochastic and not deterministic this may lead to creativity but can also lead to errors when accuracy is essential as hallucinations can be introduced due to the underlying nature of an LLM - it's just a predictor.