Large Language Model
A language model at the large end of the size scale, with many billions of internal parameters and trained on enormous amounts of text. This is the kind of model behind ChatGPT and similar tools, and it is usually what people now mean when they loosely say "AI."
That scale that allows these models to exhibit impressive fluency is also the reason behind their most important limitations. Large models need data centers to train and run, so they are typically reached through a company's closed-weight service rather than run locally. That shapes cost, environmental impact, and crucially who sees the data sent to the model, all of which bear directly on whether a community can use the tool on its own terms (see Data sovereignty).
Also, general fluency does not guarantee reliability in every context. An LLM is still probabilistic: it predicts plausible text based on patterns in its training data, and it can state a wrong answer as fluently as a right one (see Ojibwe Chat).