Large language model

Garden · not yet reviewed Established: widely used Widely discussed since 2020

The engine behind AI chat tools: a system trained on vast amounts of text to predict what words should come next, which turns out to be enough to write, summarise, translate and reason in useful ways.

Also called: LLM · the model · foundation model · GPT · frontier model

Connections

Understand first

Ideas this one builds on.

• Generative AI (garden)

Leads on to

Ideas that build on this one.

• AI writing tells (garden)
• Context window (garden)
• Hallucination (garden)
• Jagged frontier (garden)
• Model choice (garden)
• Multimodal AI (garden)
• Prompting (garden)
• Reasoning models (garden)
• Sycophancy (garden)
• Tools and connectors (garden)

Evolved into

Newer ideas that grew out of this one.

• Reasoning models (garden)

Resources that teach it

• AI Capabilities and Limitations (garden)
• Introduction to Generative AI (garden)
• Agents Course, Unit 0-1 (what an agent is) (garden)
• HuggingChat (try open models) (garden)
• LLM Course, Chapter 1: Transformer models (intro) (garden)
• The AI Agent Glossary (garden)
• An AI glossary (garden)

Competency

• Check the result

Find large language model on the commons map ↗

You may know it from

• everyday technology predictive text on a phone keyboard The same next-word guessing, trained on vastly more text and much better at keeping a thread.

In products

The same idea, under different names in different tools.

Provider	What they call it
OpenAI	GPT models
Anthropic	Claude models
Google	Gemini models
Meta	Llama models
xAI	Grok models
Hugging Face	Open models on HuggingChat

In plain words

A large language model, or LLM, is the part of an AI tool that produces the words. It was trained by reading a huge amount of text and learning, over and over, to guess the next word. Trained on enough text, that guessing becomes good enough to draft, summarise and translate.

Two facts about LLMs explain a lot of everyday behaviour. First, they produce what is likely, which is not always what is true. Second, they only know what was in their training material plus whatever you put in front of them in the conversation. They do not look things up unless the product gives them a search tool.

Chat apps such as ChatGPT, Claude and Gemini wrap a model in an interface, plus extras like file uploads, search and memory. The app and the model are different things, and one app can offer several models.

Example

You ask for “the latest figures” on your industry. The model gives tidy numbers with no source. Knowing it predicts likely text, you turn on web search or paste in the actual report, and ask again.

How it connects

• It is the engine inside most generative AI tools.
• Its guessing nature is the root of hallucination and sycophancy.
• Different models suit different jobs, which is why model choice matters.

Try it

Write down what you expect your AI tool to get wrong about your company. Then ask it: “What do you know about my company, and how confident are you?” Check its answer against what you know.

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Read this card as plain Markdown: /concepts/con-large-language-model.md. Know a parallel from your own field, or a product that implements this? Add it.