Large language model (LLM)
Also: large language models, LLMs
A program trained on enormous amounts of text so it can read and write language. ChatGPT and Claude are built on large language models.
“The judge entered the ___”
It picks a word, adds it to the sentence, and guesses again, thousands of times a second. Everything it writes, from a poem to a legal memo, is built this way. That is why it can be fluent and wrong at the same time: it is predicting what sounds right, not checking what is true.
The fuller definition
A large language model is a neural network trained on large collections of language data to model patterns in token sequences. It can generate and transform text; some model families also process other modalities.
Go deeper
- Text
The supplied passage
- Tokens
Numerical input sequence
- Output
Generated continuation
A simplified relationship, not a screenshot or a complete design.
A concrete example
A model completes a draft sentence or summarizes a supplied passage, using its training and current context.
The distinction that matters
Language fluency does not establish that a statement is true or supported by the record.
Related terms
Token
A piece of text, often part of a word, that the model actually works with. Models read and write tokens, not whole words.
FoundationsTransformer
The design most modern language models use. Its trick is letting every word in a passage look at every other word to work out what matters.
FoundationsContext window
How much text the model can hold in mind at once: your question, the documents you added, and the conversation so far. Anything outside it is invisible to the model.
Where it is taught
What actually happens when you ask AI a question?Source and further reading
Google · Machine Learning GlossaryThe definition and example are written for this library. The link gives background, official documentation or legal authority as labelled. Legal scope depends on the jurisdiction and current text; product features depend on release and settings.