The Simple Answer
A large language model is a type of AI that has been trained to understand and generate human language. It learned by processing an enormous amount of text - books, websites, articles, code, conversations - and developing the ability to predict what words should come next in any given context. Do that well enough at large enough scale, and something remarkable emerges: the ability to write, reason, explain, code, translate, and converse.
When you type a message to ChatGPT or Claude, you are sending it to an LLM. The model reads your message and generates a response, word by word, each word chosen based on what makes the most sense given everything before it.
How Does Training Actually Work?
Imagine showing someone billions of sentences, each time covering up the last word and asking them to guess what it is. They would quickly start to understand how language works - not just grammar, but meaning, context, and knowledge. They would learn that sentences about Paris often involve the Eiffel Tower, that code that starts with "def" is Python, that a question ending in "?" usually gets an answer.
That is roughly what happens when an LLM trains. The model is a massive mathematical function with billions of parameters - numbers that get adjusted during training to make better predictions. After training on enough text, those parameters encode a surprisingly deep representation of language and the world it describes.
The "large" in large language model refers to the number of parameters. Early language models had millions. Modern frontier models like GPT-4 and Claude have hundreds of billions. Scale turned out to matter enormously - models above certain size thresholds started exhibiting capabilities that smaller models did not have at all.
What Is the Difference Between an LLM and "AI"?
AI is a broad term covering any system that performs tasks that typically require human intelligence. LLMs are one type of AI, specifically focused on language. There are other types of AI - computer vision systems that identify images, recommendation systems that suggest what to watch next, reinforcement learning systems that learn to play games. What makes LLMs unusual is how general-purpose they have turned out to be: because so much human knowledge is encoded in language, a system that is very good at language turns out to be good at a surprisingly wide range of tasks.
Why Does ChatGPT Sometimes Make Things Up?
This is one of the most important things to understand about LLMs. They do not look things up in a database of facts. They generate text that is statistically likely to be correct based on patterns in their training data. Most of the time, what is statistically likely is also factually accurate. But sometimes the model generates plausible-sounding text that is simply wrong - this is called hallucination.
The model does not know when it is hallucinating. From its perspective, it is always doing the same thing: generating the most plausible next token. This is why you should verify important factual claims from LLMs rather than taking them at face value, and why AI tools with web search (like Perplexity) are better for factual research than pure LLMs.
What Are Tokens?
LLMs do not process text letter by letter or word by word. They work in tokens, which are chunks of text roughly corresponding to syllables or short words. "Unbelievable" might be three tokens. "The" is one token. "ChatGPT" might be two or three. This matters because LLMs have a context window - a maximum number of tokens they can process at once. When you hit the context limit, the model can no longer see the earlier parts of your conversation.
Modern frontier models have very large context windows - Claude can process over 200,000 tokens (roughly 150,000 words) in one context. This is why you can paste in entire documents and ask questions about them.
What Is Fine-Tuning?
A base LLM trained on general text is very capable but not necessarily good at following instructions or being helpful in conversation. Fine-tuning is additional training that shapes the model's behavior - teaching it to be helpful, to follow instructions, to avoid harmful outputs, and to have a particular personality or focus. ChatGPT, Claude, and Gemini are all fine-tuned versions of base models. The fine-tuning is a big part of what makes them feel like assistants rather than autocomplete engines.
How Are Different LLMs Different?
The main frontier models - GPT-4o (OpenAI), Claude (Anthropic), Gemini (Google), and Grok (xAI) - are all LLMs but differ in their training data, fine-tuning approaches, size, context windows, and the particular capabilities their developers have emphasized. Claude tends to be stronger at long-document analysis and writing. GPT-4o has stronger multimodal capabilities (images, audio). Gemini is deeply integrated with Google's services and real-time information.
Open-source models like Llama (Meta) and Mistral are also LLMs that anyone can download and run locally. They are generally smaller and less capable than frontier models but have no usage fees and can run on your own hardware - useful for privacy-sensitive applications or developers building products.
Will LLMs Keep Getting Better?
Yes, but the path is less clear than it was a few years ago. The "just train bigger models on more data" approach is running into diminishing returns. The advances happening now are more in the architecture (how models reason, how they use memory), training methods (reinforcement learning from human feedback, synthetic data), and how models are deployed (as agents that can take actions, not just generate text). The capabilities of AI systems are still improving rapidly but through more varied means than they were in 2020-2023.
See LLMs in action
The best way to understand LLMs is to use them. All the major models have free tiers.