Definition
Large Language Model
A large language model is an AI model trained on large text and code datasets to predict, generate and transform language.
Short definition
A large language model is a type of AI model designed to work with language. It can generate text, summarize documents, translate, classify content, write code and answer questions by predicting likely sequences of tokens.
LLMs are a major part of today's artificial intelligence ecosystem.
How it works
An LLM is trained on large collections of text, code and other structured content. During training, it learns statistical patterns in language: grammar, style, facts, reasoning traces and relationships between concepts.
At inference time, the model receives a prompt and produces a continuation. Better results often depend on clear instructions, useful context and constraints, which is why prompt engineering matters.
Example
A legal team can ask an LLM to summarize a contract clause, compare two versions of a policy or draft a first-pass explanation for a client. The output still needs review, especially when accuracy or legal responsibility matters.
Limits
LLMs can produce fluent but incorrect answers. They do not automatically know whether a generated statement is true, current or complete. Systems often reduce this risk with retrieval-augmented generation, guardrails, evaluation and human review.
The key question is not whether an LLM sounds confident, but whether it is grounded in reliable context and used in a workflow that can catch mistakes.