TL;DR: What is a large language model? A large language model (LLM) is an AI system trained on huge amounts of text to predict the next piece of a sentence, which lets it write, summarize, translate, answer questions and generate code. ChatGPT, Gemini, Claude, Llama and DeepSeek are all built on LLMs. They work by breaking text into tokens, running them through a neural network called a transformer, and producing an answer one token at a time. They are powerful pattern-matchers, not fact databases, so they can still make confident mistakes.
Almost every AI chatbot you use today runs on the same core technology, yet few people get a clear answer when they ask what is a large language model. This plain-English guide covers what an LLM is, how it is trained, how it turns your prompt into an answer, the best-known examples, what LLMs are good and bad at, and how they connect to other AI terms you keep hearing.
What is a large language model, in simple terms?
A large language model is a type of AI model that learns the patterns of human language from enormous amounts of text and uses those patterns to generate new text. Give it a prompt, and it predicts what should come next, word piece by word piece, until it has written a full reply.
Each part of the name tells you something:
- Large: the model has billions of internal settings, called parameters, and is trained on a vast collection of text. OpenAI’s GPT-3 paper in 2020 described a model with 175 billion parameters, and many newer models are bigger still.
- Language: it is trained mainly on text, such as books, websites, articles and code, so its core skill is reading and writing language.
- Model: it is a mathematical model, a neural network, that turns input text into a probability for every possible next token.
Google’s developer guide sums it up as a model that predicts the next token in a sequence. That simple job, done at massive scale, is what makes chatbots feel fluent.
How does a large language model work?
1. Text becomes tokens
An LLM does not read words the way you do. It splits text into tokens, which are often whole words, parts of words or punctuation. Each token becomes a list of numbers the network can process.
2. The transformer finds relationships
Most modern LLMs use the transformer architecture, introduced by Google researchers in the 2017 paper “Attention Is All You Need”. Its key idea, called attention, lets the model weigh how much every token relates to every other token. That is how it works out that “it” in a sentence refers to “the laptop” and not “the desk.”
3. The model predicts the next token
After processing your prompt, the model calculates the probability of each possible next token, picks one, adds it to the text and repeats. A setting often called temperature controls how adventurous those picks are. How much text it can consider at once is limited by its context window.
How LLMs are trained
Training usually happens in stages:
| Stage | What happens | Result |
|---|---|---|
| Pre-training | The model reads a vast text dataset and learns to predict missing or next tokens | A “base model” that knows language patterns and a lot of general knowledge |
| Instruction tuning | It is trained on examples of questions paired with good answers | A model that follows instructions instead of just continuing text |
| Human feedback (RLHF) | People rank model answers and the model is tuned toward the preferred ones | A more helpful, safer assistant, as described in OpenAI’s InstructGPT paper |
| Fine-tuning (optional) | Extra training on a specific domain or task | A specialized model, for example for legal or support work |
We go deeper on that last step in what is fine-tuning in AI.
Examples of large language models
Some of the best-known LLM families include:
- GPT from OpenAI, which powers ChatGPT.
- Gemini from Google, used in the Gemini app and Google products.
- Claude from Anthropic.
- Llama from Meta, released with downloadable weights.
- DeepSeek, Mistral and Qwen, which also publish open-weight models.
Many of these are now multimodal, meaning they handle images, audio or video as well as text. Our guide to multimodal AI explains how that works. If you are choosing between the big consumer apps, see is ChatGPT better than Gemini.
What LLMs are good at, and where they fall short
| Strengths | Weaknesses |
|---|---|
| Drafting emails, articles and summaries | Can state false information confidently |
| Explaining concepts in simple language | Knowledge stops at a training cutoff unless connected to search |
| Writing and debugging code | Weak at exact counting and long arithmetic without tools |
| Translating and rewriting in different tones | Can reflect biases in training data |
| Brainstorming and outlining | Has no real understanding or memory between sessions by default |
The biggest weakness is AI hallucination: because an LLM predicts plausible text rather than checking facts, it can invent sources, numbers or quotes. Developers reduce this with techniques like retrieval-augmented generation (RAG), which feeds the model real documents before it answers.
LLM vs related AI terms
- LLM vs generative AI: generative AI is the broad category of AI that creates content. LLMs are the text-focused part of it; image and video generators are other parts.
- LLM vs chatbot: the chatbot is the app you talk to. The LLM is the engine inside it, often combined with search, memory and safety layers.
- LLM vs AI agent: an agent uses an LLM to plan and take actions, such as browsing or running code, through connected tools. Protocols like MCP help connect models to those tools.
- LLM vs small language model: smaller models have fewer parameters, run on phones or laptops, and trade some capability for speed, cost and privacy.
How to get better results from an LLM
- Be specific about the task, audience, length and format you want.
- Give it the source material instead of relying on its memory.
- Ask it to show its sources or flag uncertainty.
- Break big jobs into smaller steps.
- Verify facts, figures and quotes before you publish or act on them.
For more techniques, read our guide to prompt engineering.
Frequently asked questions
Is ChatGPT a large language model?
ChatGPT is an app built on OpenAI’s GPT large language models. The LLM generates the text; the app adds features like chat history, search and file uploads.
Do LLMs understand what they say?
That is debated. LLMs capture deep statistical patterns in language, which lets them reason through many problems, but they do not experience the world and can fail in ways a person would not.
Can I run a large language model on my own computer?
Yes. Open-weight models in smaller sizes can run locally on a capable laptop or desktop using free tools, though the largest models still need data-center hardware.
Bottom line
A large language model is a giant next-token predictor trained on vast amounts of text, and that simple mechanism powers the chatbots, writing tools and coding assistants reshaping work. Knowing how it works, from tokens to transformers to training, makes it easier to use LLMs well and to spot when they are guessing.
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