Glossary
Large Language Model (LLM)
A large language model is a system trained on a vast volume of text so that it has learned language patterns and can generate, summarize, translate, and reason in language. It is the engine of many of today’s AI systems, but it is not a product on its own.
Also called مدل زبانی بزرگ
A large language model is, at root, a predictor: given the text it has received, it produces the most likely continuation. That simple mechanism, at large scale, produces behavior that looks like understanding and reasoning.
Two limits matter for organizational use. First, the model only knows what was in its training data, and only up to a cutoff date; your company’s data from today is not in it. Second, the model inherently “does not know that it does not know” and may produce a fluent but wrong answer.
That is why reliable organizational systems connect the model to real data, validate the output against the data, and keep action paths under control.
Common questions
- Does the language model learn our data?
- In ordinary API use, no; the data is used in the reply, but the model is not retrained on it.
- Which model is better?
- It depends on the use; for structured work a smaller, cheaper model is usually enough.
Let’s make your processes agentic
Where do we start?