What Is an LLM? A Plain-English Guide
Large language models are everywhere right now — powering ChatGPT, Claude, Gemini, and dozens of other AI tools you may already use. But what actually is an LLM, and how does it work? This guide explains it in plain English, no math required.
The one-sentence definition
An LLM (large language model) is a computer program trained on vast amounts of text that can read, write, and reason about language — in any direction you point it.
What "large" means
The word "large" refers to two things:
- The training data. LLMs are trained on hundreds of billions of words — books, websites, code, scientific papers, and more. When a model reads all of that, it builds up a statistical map of how language works: which words follow other words, how sentences are structured, what ideas are related.
- The model itself. LLMs are measured in "parameters" — roughly, the number of adjustable connections inside the model. Today's frontier models have hundreds of billions of parameters. More parameters (usually) means a smarter, more nuanced model.
How does an LLM actually work?
The short version: an LLM predicts what comes next.
When you send a message to an LLM, it reads every word you wrote and asks: given everything I've read in training and everything in this conversation, what is the most likely, most useful thing to say next?
It does this one word (actually one "token") at a time. Each word it generates becomes part of the input for the next word. This is called autoregressive generation.
The impressive part is that "predicting the next word really well" turns out to be surprisingly powerful. A model that can do this at scale learns grammar, facts, logic, code, and even some reasoning — all as a byproduct of a single goal.
What can LLMs do?
Modern LLMs are remarkably versatile. Common uses include:
- Writing and editing — drafting emails, blog posts, reports, or marketing copy
- Coding — generating, explaining, and debugging code in dozens of languages
- Research and summarization — reading long documents and surfacing the key points
- Question answering — answering factual questions (with some caveats — see below)
- Translation — moving text between languages
- Data extraction — pulling structured information from unstructured text
- Reasoning — working through multi-step problems in math, logic, and planning
The right model depends on your task. LLMReference tracks which models are best for coding, writing, reasoning, and more.
What can't LLMs do?
They can hallucinate. LLMs sometimes generate confident-sounding text that is factually wrong. This is especially common on obscure topics, recent events, or specific numbers. Always verify important facts.
Their knowledge has a cutoff. Most LLMs are trained on data up to a specific date and don't know about things that happened after that unless they have access to search tools.
They don't "understand" in the human sense. LLMs are very good at pattern-matching and generation, but they don't have intentions, feelings, or awareness. They don't "know" things the way a person does — they produce outputs that look like knowledge.
They can be expensive at scale. LLM APIs charge per token (roughly per word). Running millions of queries adds up. See how LLM API pricing works or compare cheapest LLM APIs if cost is a factor for you.
How do I try one?
The fastest way to try an LLM is via a free chat interface:
- ChatGPT (OpenAI) — the most widely used
- Claude (Anthropic) — strong at long documents and analysis
- Gemini (Google) — integrated with Google Workspace
- Llama (Meta) — available via many third-party apps and self-hosted
If you want to use an LLM in your own software via API, you'll need an API key from the provider of your choice. LLMReference's model directory covers 1,700+ models with pricing, context window, and provider availability for each.
LLM vs GPT vs AI — what's the difference?
You'll hear several terms used loosely. Here's a quick map:
| Term | What it means |
|---|---|
| AI | Broad category: any software that performs tasks normally requiring human intelligence |
| LLM | A specific type of AI: a large language model trained on text |
| GPT | A specific LLM architecture invented by OpenAI (GPT = Generative Pre-trained Transformer). Often used informally to mean "any LLM." |
| ChatGPT | A product built by OpenAI on top of their GPT models |
| Claude, Gemini, Llama | Competing LLMs from Anthropic, Google, and Meta respectively |
All of the major AI chat tools you've heard of are LLMs (or are built on LLMs). They just use different architectures, training data, and fine-tuning approaches.
Which LLM is best?
It depends on what you need. LLMs differ significantly in:
- Capability — some are better at coding, others at writing, others at reasoning
- Context window — how much text they can read at once (from ~4K to 2M+ tokens)
- Cost — from free hosted tiers to $15+ per million tokens for frontier models
- Availability — some are API-only, others are open-weight (downloadable)
LLMReference ranks the best LLMs by use case so you don't have to benchmark them yourself.
Best LLMs by use case