LLM
Large Language Model
A statistical model trained to predict the next token, which makes it shockingly good at language tasks and unreliable at anything that requires guaranteed correctness.
An LLM is a model trained on enormous amounts of text to do one thing: predict the next token (roughly, the next word fragment) given everything that came before. Stack enough of that prediction together and you get fluent answers, summaries, translations, and code. That is the whole trick. It is not reasoning in the human sense, it is very good pattern completion.
This matters because it explains both the magic and the limits. An LLM has no memory of your business, no knowledge past its training cutoff, and no built-in guarantee that the fluent answer it produces is true. It will state a wrong fact with exactly the same confidence as a right one. Treat it as a tool, not an oracle.
The LLM is the wrong tool when you need deterministic, auditable output: tax calculations, regulatory logic, anything where “usually correct” is a liability. The right move is to wrap the model with the boring engineering around it, retrieval for facts, validation for output, and a human in the loop where the cost of being wrong is high. We build exactly that under Artificial Intelligence.
The honest summary: an LLM is a probabilistic text engine. Used for the right jobs (drafting, classification, extraction, search over your own data) it is a genuine force multiplier. Used as a source of truth, it is a confident liability.