In one sentence
A LLM (Large Language Model) is an artificial intelligence program trained on billions of sentences to predict the next word. This is what powers ChatGPT, Claude, Gemini, Mistral and all the others.
You don't need to know more than this to use ChatGPT or Claude daily. The key thing to remember: a LLM predicts text, it doesn't "understand" like a human. It's very good at summarizing, translating, writing, coding. But it can invent things that are false (called "hallucinations") and it knows nothing about events after its training cutoff date.
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How does it really work?
When you write to a LLM "The sky is…", it computes the probability of each possible word:
Probabilities of next word after 'The sky is'
It picks a word (usually the most probable, but not always to stay creative), adds it to the sentence, and starts over for the next word. That's it.
This process is called inference or autoregressive generation. For each generated word, the LLM rereads the entire conversation to guess the next one.
The ingredients of a LLM
Three things make a good LLM:
- An architecture: the "shape" of the neural network. Today, almost all use the Transformer architecture, invented by Google in 2017.
- Training data: thousands of billions of words from the web, books, code, scientific papers.
- Computing power: training GPT-5 cost over $500 million and required tens of thousands of GPU cards for months.
Model sizes today
Here are the approximate sizes of major models in 2026:
Model size (in billions of parameters)
⚠️ Note: a bigger model is not always better. Many use a trick called MoE (Mixture of Experts) where only a portion of parameters activates per request. It's faster and cheaper. Mistral Large 3 for example, with "only" 123B parameters, competes with models 10x larger.
How does a LLM learn?
Training a LLM happens in 3 steps:
1. Pre-training (the "massive reading")
The model reads billions of pages of text and learns to predict the next word. It's the longest step (several months) and the most expensive.
2. Supervised fine-tuning
It's shown human examples of good responses: "If asked X, respond with Y this way". This teaches it to be useful, not just imitate the Internet.
3. RLHF (Reinforcement Learning from Human Feedback)
Humans compare two model responses and say which is better. The model learns to favor preferred answers. This is what makes Claude polite and helpful rather than cynical like some parts of Reddit.
Evolution since 2017
Major LLM milestones
The Transformer is born
Google publishes 'Attention Is All You Need', the paper that changed everything.
BERT and GPT-1
First large models. BERT understands, GPT generates.
GPT-3 (175B params)
First model that 'understands everything'. AI lab hype begins.
ChatGPT
AI goes mainstream. 100 million users in 2 months.
GPT-4, Claude, Llama
Power race. Llama launches the open-source wave.
Reasoning models
OpenAI o1, Claude Sonnet: LLMs learn to 'think' before answering.
Multimodal everywhere
Text + image + audio + video in the same model. Gemini, GPT-4o.
Current frontier
GPT-5, Claude Opus 4.7, Gemini 3. Models with 1500-1800B params, widespread MoE, autonomous agents.
LLM limitations
Now that you know how it works, here's what a LLM cannot do (yet):
Real use cases
Concretely, here's what LLMs do really well today:
- ✅ Writing: emails, articles, summaries, translations
- ✅ Coding: generating code, debugging, explaining
- ✅ Summarizing: condensing a 100-page report into 5 bullets
- ✅ Rephrasing: adapting a text for different audiences
- ✅ Brainstorming: throwing 10 ideas on a topic
- ✅ Learning: explaining a concept at different levels
- ✅ Converting: transforming unstructured text into JSON, tables, etc.
And what remains risky:
- ⚠️ Precise calculations (use a calculator or tool)
- ⚠️ Recent facts (without web connection)
- ⚠️ Nuanced political opinions
- ⚠️ Medical/legal advice (always validate with a pro)
How to choose your LLM?
Good question — there are 30+ available, all different.
For what use, which model?
| If you want… | 2026 recommendation | |
|---|---|---|
| Best for writing | Literary quality, perfect English | Claude Opus 4.7 |
| Best for coding | Complex code generation | Claude Opus 4.7 or GPT-5 |
| Multimodal (image, video) | Image analysis, huge context | Gemini 3 Pro (1M tokens) |
| Sovereign and GDPR | Hosted in Europe | Mistral Large 3 (FR) |
| Free self-hostable | Run at home | DeepSeek V3 or Llama 4 |
| Very cheap | Low-cost API | Claude Sonnet or Gemini Flash |
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Quiz: did you understand?
What does LLM mean?
Going further
Now that you know what a LLM is, you can explore:
- 🎯 The model comparison: see which one fits your use case
- ⚡ Prompt engineering: learn how to talk to LLMs
- 💰 Pay or not: choose between free and paid versions
And keep in mind: a LLM is a very powerful tool, but you have the brain. Use it as a copilot, not as an oracle. ✨