What it is and how it works, explained with an analogy

Imagine a person who has read millions of books, articles, and conversations. They didn't memorize them: they absorbed the ways words combine with one another. If you say "the cat climbs onto the...", they know "roof" is far more likely than "fridge." An LLM does exactly this, on a gigantic scale: it generates answers one word at a time, each time choosing the one that best fits what came before.

Under the hood, it works like this:

  • Training happens through deep learning: the system reads text in enormous quantities and adjusts billions of parameters, which you can picture as tiny knobs that settle into place until sensible sentences come out.
  • The architecture is called a transformer: its main strength is looking at all the words in a sentence together and figuring out which ones matter most to the meaning. This ability to focus on the relevant points is called attention.
  • The whole process, ultimately, is one long chain of mathematical calculations: no consciousness, no human understanding — language statistics, done very well.

A concrete example

When you ask ChatGPT to "write me an email to cancel an appointment," the system doesn't pull a ready-made email from an archive. It generates it word by word, on the spot, based on the ways millions of similar emails were written. That's why you can ask for it to be more formal, shorter, or in English: it isn't copying, it's reconstructing.

What you can actually do with an LLM

  • Write and rewrite: emails, texts, posts, in the tone you ask for.
  • Summarize long documents into key points.
  • Translate while keeping the meaning and style.
  • Have it explain difficult concepts in simple words.
  • Analyze a text, for example to figure out whether a review is positive or negative (in jargon this is called sentiment analysis).

A common misconception

An LLM doesn't "know" things the way an encyclopedia does, and it doesn't search a database of facts: it predicts the most plausible text. That's why it sometimes gets things wrong with great confidence, inventing details that sound true — the phenomenon of hallucinations. For important information, ask it for sources or verify elsewhere: it's an excellent writing and reasoning assistant, not an oracle.

Frequently asked questions

What's the difference between an LLM and ChatGPT?

The LLM is the engine, ChatGPT is the complete car. ChatGPT, Claude, and Gemini are applications built around an LLM, with the chat interface, conversation memory, and behavioral rules.

Why does an LLM sometimes make up answers?

Because it doesn't consult an archive of facts: it generates the most probable text. When it lacks solid information, the "probable" text can be false but believable. These are hallucinations: they're reduced by asking for sources or connecting the AI to web search.

Does an LLM really understand what it says?

Not in the human sense. It recognizes and reproduces language patterns with such precision that it seems like understanding. For everyday practical use the difference often goes unnoticed, but it's worth remembering when important decisions are involved.

Do LLMs work well in Italian?

Yes, the major ones (ChatGPT, Claude, Gemini) handle Italian well, even though the amount of English text they're trained on is greater: on very niche topics, they sometimes give richer answers in English.

Where are LLMs used besides chatbots?

Anywhere you need to work with language: voice assistants, translators, search engines, programs for writing code, analysis of business documents, automated customer support.