TL;DR: A large language model (LLM) is a kind of AI system that has been trained on enormous amounts of human-written text and learned to predict what word should come next. From that one simple objective — “predict the next word” — the model develops the ability to read, write, summarize, translate, answer questions, write code, and hold conversations. ChatGPT, Claude, Gemini, and Llama are all LLMs. They are the technology behind almost everything that’s happened in AI since late 2022.
Why read: If you use AI tools, knowing how LLMs work in plain English is the difference between being intimidated by the technology and being able to actually use it well. This guide skips the technical jargon and gives you the mental model that matters.
Best for: Anyone new to AI, anyone trying to explain LLMs to a non-technical colleague, or anyone who has been using AI tools without really understanding what’s happening under the hood.
Skip if: You build LLMs for a living. Daily AI fundamentals in our free Beginners in AI newsletter.
A large language model is the most important piece of technology to learn about in 2026. Almost every AI tool you’ve heard of — ChatGPT, Claude, Gemini, Copilot, Perplexity, Cursor, Midjourney’s text understanding, the chatbot on your bank’s website — runs on top of an LLM. Understanding LLMs is the difference between feeling lost in the AI conversation and being able to participate in it intelligently.
The good news: the core idea is simpler than you might think. The technical version is genuinely complex; the conceptual version is one sentence. We’ll build it up step by step.
What is a large language model in one sentence?
A large language model is an AI system that has been trained on enormous amounts of human-written text and learned to predict what word should come next in any sequence of words.
That’s it. Everything else — the chat ability, the coding, the summarization, the translation, the seeming-to-reason — emerges from that one training objective: given some words, predict the next word.
It sounds too simple to be impressive. The surprising part is that learning to predict the next word, well enough, on enough text, produces a system that behaves as if it understood language. That’s the deep finding of the past five years of AI.
How does “predict the next word” turn into “answer my email”?
Imagine you read 100 million books, every news article ever written, the entirety of Wikipedia, all of Reddit, every coding tutorial on the internet, transcripts of millions of conversations, and a huge fraction of the world’s educational material. Imagine you read all of that with the single task of getting good at predicting what word comes next in whatever sentence you happen to be in.
Now I show you the partial sentence:
“The capital of France is…”
What’s the next word? “Paris.” You don’t know that because you’ve memorized a fact — you know it because that sequence of words has appeared millions of times in your training data, almost always followed by “Paris.” The LLM is doing the same thing.
Now I show you a harder one:
“Dear hiring manager, Thank you for the interview on Tuesday. I wanted to follow up with…”
The next word is probably something like “a” or “some” or “the.” The word after that is probably “thoughts” or “additional” or “quick.” The model doesn’t pick one randomly — it picks the word that has the highest probability based on everything it’s read. And it keeps doing this, one word at a time, until it’s produced an entire follow-up email.
That’s how an LLM “answers your email.” It’s really just predicting the most likely next word, over and over, given the context of what you wrote and what good email replies have historically looked like.
Why does “large” matter so much?
Two things scale with the word “large”:
- The amount of training data. Modern LLMs train on trillions of words. That’s more text than any human could read in a thousand lifetimes.
- The size of the model itself. An LLM is, internally, a giant collection of numbers called “parameters” that determine how the model converts input words into output predictions. GPT-3 (2020) had 175 billion parameters. Modern frontier models — Claude Opus 4.7, GPT-5.5, Gemini 2.5 — reportedly run in the hundreds of billions to over a trillion parameters.
The reason “large” matters is empirical: bigger models trained on more data perform measurably better at the next-word prediction task. They also develop unexpected new abilities that smaller models lack — the ability to write code, the ability to follow multi-step instructions, the ability to reason through math problems. These are called “emergent capabilities” and they’re the central reason the AI industry has been racing to build bigger models.
What does training an LLM actually look like?
Training has two big phases.
Phase 1: Pre-training
The model is shown text and asked to predict the next word. When it’s wrong, the model’s internal parameters get adjusted slightly to make the correct word more likely next time. This happens billions of times, on trillions of words, using thousands of specialized AI chips (mostly NVIDIA GPUs). Pre-training a frontier LLM in 2026 costs hundreds of millions of dollars in compute alone.
What comes out of pre-training is sometimes called a “base model.” A base model is competent at predicting next words but not particularly useful as a chatbot. It will happily complete “Once upon a time, there was a…” with a story, but it doesn’t know to respond helpfully when you ask it a question.
Phase 2: Fine-tuning and alignment
After pre-training, the model is fine-tuned to be useful. Human trainers and other AI systems show the model thousands of examples of how to follow instructions, how to be helpful, how to avoid harmful outputs, how to admit when it doesn’t know something. This process — sometimes called RLHF (Reinforcement Learning from Human Feedback) and various successors — is what turns a base model into a chat-style assistant.
The result is what you actually interact with: a model that is competent at predicting the next word AND has been trained to use that ability in ways that follow your instructions and are broadly helpful.
What is the difference between an LLM and ChatGPT (or Claude)?
This trips up almost everyone new to AI. The short version:
- An LLM is the underlying AI model. It’s the trained system that does the next-word prediction.
- ChatGPT, Claude, Gemini, and Copilot are products built on top of LLMs. They include the LLM plus a chat interface, a memory system, a tool-use system (like web search, code execution, image generation), safety filters, account management, billing, and so on.
The LLM is the engine; the product is the car. You can have the same engine in different cars. OpenAI’s ChatGPT product uses OpenAI’s GPT models. Anthropic’s Claude product uses Anthropic’s Claude models. Microsoft Copilot has historically used a mix of OpenAI and Microsoft models. Many other products use LLMs through the API of one of the major providers without making their own.
What are the major LLMs in 2026?
| LLM family | Made by | Best for |
|---|---|---|
| Claude (Opus 4.7, Sonnet 4.6, Haiku 4.5) | Anthropic | Long-context analysis, agentic coding, careful reasoning |
| GPT (GPT-5.5, GPT-5.5 mini, etc.) | OpenAI | General-purpose; ChatGPT consumer product; agent / computer-use work |
| Gemini (Gemini 2.5 Pro and Flash, etc.) | Google DeepMind | Multimodal (image, video, audio), tight integration with Google products |
| Llama | Meta | Open-weights; can be downloaded and run locally |
| Mistral models | Mistral AI (France) | European alternative; open-weights options |
| Qwen | Alibaba (China) | Strongest current open-weights options from China |
| DeepSeek | DeepSeek (China) | Reasoning-focused open-weights models |
| Grok | xAI (Elon Musk) | Tightly integrated with X (Twitter); near-real-time web context |
For a fuller comparison see Every AI Model Worth Knowing in 2026.
How do LLMs “know” things?
This is the right question to ask, and the honest answer takes some unpacking.
LLMs don’t have facts stored in a database. They don’t look anything up by default. They have, during training, observed huge amounts of text in which various facts were repeated. The information about those facts gets encoded into the model’s parameters — not as discrete pieces of data, but as patterns that influence what words it will predict.
So when you ask “What is the capital of France?” the model isn’t looking up an entry. It’s using its parameters — which encode patterns from millions of texts that say “Paris is the capital of France” or use the words “Paris” and “France” near each other in particular ways — to predict that “Paris” is the most likely next word.
This explains two things about LLMs:
- Why LLMs are good at well-documented facts and worse at obscure ones. The capital of France is everywhere; the secondary school principal of an obscure school in 1987 is nowhere.
- Why LLMs “hallucinate.” When the model doesn’t have strong training-data support for an answer, it still generates the most-likely-sounding next words. That can produce confident-sounding answers that are factually wrong. The model doesn’t know it’s wrong; it’s just doing what it always does.
For more on the hallucination problem see our glossary entry on AI hallucination.
What is a token, and why does it matter?
LLMs don’t actually predict whole words — they predict “tokens.” A token is a chunk of text. Common words are usually one token; rare words and long words are often split into multiple tokens. For example:
- The word “the” = 1 token.
- The word “hippopotamus” ≈ 3 tokens.
- A paragraph of typical English text ≈ 60–100 tokens.
- A short article ≈ 1,000 tokens.
- A typical email ≈ 200–500 tokens.
You’ll see tokens come up in two important contexts:
- Pricing. LLM APIs charge per million tokens. Claude Opus 4.7 costs $5 per million input tokens and $25 per million output tokens. That’s roughly $0.005 per page of input you send the model, and $0.025 per page of output you get back.
- Context window. Every LLM has a limit on how many tokens it can “see” at once. Modern flagship models have context windows of 200,000 to 1,000,000 tokens — large enough to load whole books or entire codebases.
For more, see our glossary entry on tokens and our glossary entry on context windows.
Can LLMs actually reason?
This is the most-debated question in AI today. The careful answer:
LLMs are clearly doing something that looks like reasoning. They can work through multi-step math problems. They can solve coding challenges they’ve never seen before. They can follow chains of logical inference. The newest models with explicit “extended thinking” or “reasoning” modes (Claude Opus 4.7, GPT-5.5, Gemini 2.5 Pro) do this even more visibly — they generate internal monologues working through a problem before producing a final answer.
Whether this counts as “real” reasoning depends on what you mean by the word. The LLM is still, at its core, predicting next tokens. But the patterns it’s learned to predict include patterns that look very much like step-by-step logical thinking. In practice, modern LLMs solve many problems that, a few years ago, would have been considered reliable tests of human-style reasoning.
The frontier of AI research right now is largely about making this reasoning more reliable, longer-running, and more capable of using tools (web search, code execution, image generation) along the way.
What can LLMs not do?
- Know things that happened after their training cutoff. Each LLM is trained on data up to a specific date and doesn’t know about events after that. (Newer models work around this by being given web-search tools.)
- Reliably tell you they don’t know. Models are improving at this, but the hallucination problem is fundamentally tied to how they work.
- Remember things between conversations by default. Each conversation typically starts fresh. (Newer products like ChatGPT and Claude have memory features that work around this.)
- Take actions in the world without tool access. An LLM by itself can only produce text. Products that wrap LLMs can add tools (web browsing, code execution, computer use) but the LLM itself is a text-in-text-out system.
- Continually learn from your conversations. The model’s parameters are fixed after training. New knowledge has to come from the prompt context or from a future training run.
- Be perfectly safe or unbiased. Training data reflects human writing, with all the biases that includes. Alignment work mitigates but doesn’t eliminate these issues.
What’s the difference between an LLM and an “AI agent”?
An LLM is a text-in-text-out system. An AI agent is an LLM wrapped in a feedback loop with tools and a goal.
The pattern looks like: the agent takes a high-level goal (“book me a flight to Tokyo”), uses the LLM to think about the next step, calls a tool (a flight-search API), gets results back, feeds them to the LLM again, decides on the next step, and continues until the goal is achieved. The LLM is the brain; the agent is the body and the to-do list.
For more, see What Are AI Agents? and AI Agents for Beginners.
FAQ
Is an LLM the same thing as “AI”?
No. AI is a much broader field that includes computer vision, robotics, recommendation systems, game-playing AI, and many other techniques. LLMs are one particular kind of AI — the one that’s been getting all the attention since 2022. When most people say “AI” in 2026 they often mean “LLM-powered AI” specifically, but it’s worth knowing the distinction.
Is an LLM the same as a transformer?
An LLM is built on transformer architecture. The transformer is the specific neural-network design (introduced in the 2017 “Attention Is All You Need” paper) that all modern LLMs use. Every major LLM today — GPT, Claude, Gemini, Llama — is a transformer-based model. You can have transformers that aren’t LLMs (image and video transformers, for example), but you can’t have a modern LLM that isn’t a transformer.
Are LLMs “just autocomplete”?
Technically the underlying mechanism is similar — predict the next word given the prior words. But that’s like saying the human brain is “just a bunch of neurons firing.” The behavior produced is qualitatively different. Modern LLMs solve tasks (writing working code, summarizing complex documents, explaining concepts in plain language) that “autocomplete” never could. The phrase undersells what current models actually do.
Why do LLMs sometimes make things up?
Because the model’s only training signal is “predict the next word.” When it doesn’t have strong training-data support for a fact, it still produces the most likely-sounding sequence of words. That can yield confident-sounding outputs that are wrong. Newer models with reasoning modes and search-tool access reduce this but don’t fully eliminate it.
Will LLMs replace search engines?
They’re already changing how many people search. Bing, ChatGPT, Claude, and Perplexity all blend search with LLM-generated summaries. The honest read: LLM-driven search is taking share of search traffic away from traditional Google-style ten-blue-links searches. Whether one fully replaces the other depends on technical reliability and user habit changes that are still in motion.
How much energy does running an LLM use?
Training a frontier LLM uses enormous energy — comparable to the lifetime usage of a small city. Each query you make to an already-trained LLM uses much less energy — comparable to a moderate search query or a brief video stream. The bigger sustainability question is the cumulative impact of training increasingly large models, which is an active industry conversation.
Can I run an LLM on my own computer?
Some yes, the frontier ones no. Smaller open-weights LLMs (Llama 3 8B and 70B, Mistral, Qwen, DeepSeek-R1) can run on a high-end laptop or desktop with a good GPU. Tools like Ollama and LM Studio make this approachable. Frontier-class LLMs like Claude Opus 4.7 and GPT-5.5 are far too large to run on consumer hardware — you access them through their providers’ APIs or product interfaces.
What should I use an LLM for first?
The classic on-ramps: write a difficult email, summarize a long document, draft a meeting agenda, debug a piece of code, plan a trip, explain a complicated concept to you in plain language, brainstorm ideas, translate something. The lowest-friction way to start is to keep a chat tab open and try the LLM on real work for a week.
The bottom line
A large language model is an AI system that learned to predict the next word in a sequence by training on enormous amounts of text. That one objective — predict the next word, well — turns out to produce a system that can read, write, summarize, translate, code, and converse in ways that would have been considered science fiction a decade ago.
The technology has limits. LLMs hallucinate. They don’t know what they don’t know. They forget between conversations unless given a memory system. They reflect biases from their training data. They use real energy. None of that changes the fact that LLMs are the most important new piece of general-purpose technology this decade, and learning to use them well is something almost everyone should be investing in.
For more, see our glossary entry on LLMs, Every AI Model Worth Knowing in 2026, How to Use Claude AI: The Complete Beginner’s Guide. Daily AI fundamentals in our free Beginners in AI newsletter.
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Sources
- Vaswani et al., Attention Is All You Need (2017) — the foundational transformer paper.
- Anthropic, Research publications — foundational papers on Claude’s training and alignment.
- OpenAI, Research publications — GPT family technical reports.
- Stanford HAI, AI Index Report — annual industry data on AI capabilities and trends.
- Anthropic, Introducing Claude Opus 4.7 — current Anthropic flagship.
- Andrej Karpathy, Intro to Large Language Models — the most-recommended free lecture series.
- Ethan Mollick, One Useful Thing — ongoing practical commentary on what LLMs can and cannot do.
- Anthropic Academy and OpenAI Cookbook — official learning resources from the two largest LLM providers.