1. Is AI the same as an LLM?
No — and that's the first confusion to clear up. "AI" gets thrown around constantly, but there are two very different things behind it.
"Classic" AI has existed for decades: it's the algorithms that recommend a product, filter spam, or optimize a delivery route. Useful, but specialized — each system does one thing, the one it was coded for.
The recent breakthrough is generative AI: a single system can write an email, summarize a 40-page PDF, translate, code, and analyze a sales table — without being programmed for any of those tasks specifically. That versatility is what changes the game. And an LLM is precisely the engine powering this generative AI for text: a subset of the big word "AI," not a synonym.
- AIAny system that mimics an "intelligent" task — since the 1950s.
- Machine LearningAI that learns from examples rather than hand-coded rules.
- Deep LearningMachine learning using large neural networks.
- LLMA neural network specialized in language. The engine behind assistants like ChatGPT or Claude.
2. What is an LLM, concretely?
An LLM is a model that has "read" a massive portion of everything written online, and learned one thing: guessing the next word. You give it a beginning, it predicts the most likely continuation, word by word.
That sounds a lot like the autocomplete on your phone. The difference is scale: after ingesting so much text, the model ends up "understanding" enough of language structure to summarize, argue, translate, or write code — capabilities that emerge without being explicitly programmed.
« The customer wants an amp … »
Next word — candidates ranked by probability
- powerful41 %
- home-cinema28 %
- second-hand11 %
The right mental model to keep: an LLM doesn't recite a database, it generates. It doesn't go "look up" a stored answer — it reconstructs one on the fly. That's its strength (it adapts to any request) and its weakness (it can reconstruct something wrong — more on that below).
3. Which LLMs are the most well-known?
The market has organized around a few families. Versions come out fast (every few months) — what matters is knowing who does what:
- OpenAI — ChatGPT: the most well-known to the general public, the one that started the wave in late 2022.
- Anthropic — Claude: focused on reliability, reasoning, and "serious" work (writing, code, analysis). This is the one I build everything on (see the dedicated guide).
- Google — Gemini: integrated into the Google ecosystem, very strong on long documents and multimodal (text + image + audio).
- Meta — Llama: "open weights," meaning you can host it yourself.
- Mistral: the French/European champion, also partly open — the sovereignty argument when hosting your data in Europe matters.
- DeepSeek: Chinese, open, formidable on the performance-to-price ratio (but raises data privacy questions).
- xAI — Grok: Elon Musk's model, plugged directly into X.
- Microsoft — Copilot: the AI integrated into Windows and the Office suite, and a widely used "Copilot" for code. Under the hood, partner models (including OpenAI).
- Perplexity: less a model than an answer engine — it searches the web and responds with cited sources.
4. What can an LLM NOT do?
This is the section people skip most often — and it's the one that prevents nasty surprises.
- It's not a search engine or a database. Because it generates instead of retrieving, an LLM can invent a wrong answer with total confidence — that's called a hallucination. On numbers, product references, dates: always verify.
- By default, it's not connected to the real-time web or your data. For it to read your emails, your Shopify, or your ERP, you have to explicitly connect it (that's the whole point of another guide).
- It's a copilot, not an oracle. Used well, it saves an enormous amount of time; used with blind trust, it does damage. The entire method is built around that.