What is an LLM? Large language models explained

What is an LLM in simple words

An LLM is a program that has read a very large collection of text and learned how language fits together. Given some words, it works out what should come next.

IBM describes LLMs as a category of deep learning models trained on immense amounts of data, able to understand and generate natural language and other content.

Harvard Business School lists the kind of text involved: books, articles, websites and research papers.

So the large language model meaning comes down to two things: a large model, and a large amount of text.

Large language model explained in how it works

Large language model explained in steps: text is split into small pieces called tokens, which can be whole words or parts of words.

During training, the model sees enormous amounts of text and learns which tokens tend to follow which.

How does a large language model work once it is trained?

You write a prompt, the model turns it into tokens, and it generates a reply one token at a time, each choice shaped by the prompt and by everything it learned.

Size matters. IBM notes that GPT-3, released in 2020, had 175 billion parameters, which helped establish LLMs as a major force in AI. Parameters are the adjustable values the model tunes while learning.

What LLMs do well and badly

LLMs are strong at language work: summarizing long documents, drafting replies, sorting messages, pulling facts out of text and translating. A large language model for coding can also suggest and explain code.

They are weaker where exact facts matter. A model can write a confident answer that is wrong, because it predicts likely words rather than checking truth. See AI hallucination for why this happens.

Good design adds checks. Many teams pair the model with their own documents through retrieval augmented generation and keep a person in the loop for key steps.

Large language model example and business uses

• Route emails

  Read each message and send it to the right person.

• Draft support replies

  Write answers from past tickets.

• Extract from documents

  Pull key terms from contracts and invoices.

• Summarize calls

  Write follow-ups into the CRM.

A well-known large language model example is the model behind a chat assistant, such as ChatGPT. It is generative AI built on an LLM; see generative AI for the wider category.

In a business, the model is rarely used alone.

It sits inside a workflow, connected to your CRM, inbox or files, so the output triggers real work. Our AI automation guide shows how this fits together.

Building a large language model from scratch takes vast data and computing budgets. Most companies instead build on an existing model and spend their effort on the workflow around it.