Large Language Model (LLM)
LLM stands for large language model.
An LLM is a type of model trained on large amounts of text so it can understand and generate language-like output such as answers, summaries, code, and conversations.
What it does
An LLM predicts and generates tokens based on patterns learned from training data.
In practice, that lets it:
- Answer questions
- Summarize text
- Draft or rewrite content
- Generate code
- Classify or extract information
- Hold conversational interactions
Core concepts
Language model
A language model works by predicting likely next tokens in context.
That token prediction behavior is what powers chat, writing, summarization, and many other text tasks.
Large-scale training
The large in LLM refers to the scale of training data, model parameters, and compute involved in building the model.
Product vs model
An LLM is the underlying model, not the full product experience.
Products such as ChatGPT are built on top of LLMs, while organizations such as OpenAI develop and serve LLM-based systems.
Prompts
LLMs respond to a prompt, which is the input or instruction given to the model.
Common use cases
- Conversational assistants
- Search and question answering
- Summarization
- Translation and rewriting
- Code generation and explanation
- Information extraction and classification
Practical notes
- Not every AI system is an LLM, but many modern generative text products are.
- LLMs can sound confident even when wrong, so output quality still needs judgment and verification.
- The usefulness of an LLM depends heavily on the quality of the prompt, the surrounding system, and the task itself.
- LLMs are one part of the broader AI/ML landscape.
Frequently Asked Questions
Is an LLM the same as AI?
No. An LLM is one type of model within the broader AI/ML space.
Is ChatGPT an LLM?
Not exactly. ChatGPT is a product experience built on top of LLMs.
Why do prompts matter for LLMs?
Because the model’s output depends heavily on the input context and instructions it receives through the prompt.