Generative AI: what it is and how it works

A technology that creates, not just recognizes

For decades, artificial intelligence mostly did one thing: recognize. Recognize a face in a photo, a spam email, a sentence spoken on the phone. Generative AI flips the perspective: instead of analyzing existing content, it creates new content.

Ask it for a cover letter, and it writes one. Describe an image — “an astronaut cat in watercolor style” — and it draws it. Paste in a twenty-page contract, and it summarizes it in ten lines. All starting from a simple request written in natural language: the prompt.

How it works: predicting the next word

Behind tools like Claude, ChatGPT or Gemini sits a large language model (LLM): a neural network with billions of parameters, trained by reading an amount of text comparable to millions of books.

The working principle is surprisingly simple to state: predict the next word.

Practical example: when you read “the cat meows and the dog…”, your brain instinctively completes it with “barks”. An LLM does the same thing, but at an enormous scale: for every word it generates, it weighs the entire context of the conversation and computes the most likely continuation. Repeating that prediction billions of times, word after word, it builds articulate, coherent, relevant answers.

During training, the model doesn’t memorize the pages it reads the way a database would: it learns patterns — grammar, style, logical connections, recurring factual knowledge — and uses them to generate text that never existed before.

What it can actually do

Generative AI is used every day to:

• Write and rewrite: emails, articles, posts, translations, texts adapted to different tones and audiences.
• Summarize and explain: long documents condensed into key points, complex concepts explained at different levels.
• Code: generating and fixing software, today among the most popular uses of all.
• Create images, audio and video: from a text description to a finished piece of media.
• Brainstorm: propose ideas, alternatives and angles you hadn’t considered.

The limits you need to know

The most important one has a curious name: hallucination. Because the model always generates the most likely continuation and not necessarily the true one, it sometimes produces invented information in a perfectly confident tone: a wrong date, a quote that never existed, a made-up source.

Practical example: ask a model for the biography of a little-known person and you may get plausible but inaccurate dates and events, mixed with true facts. The text sounds right — and that’s exactly the risk.

The golden rules for smart use:

1. Verify important factual claims against authoritative sources.
2. Don’t share personal or confidential data in conversations.
3. Use AI as a collaborator, not an oracle: responsibility for the final result stays with you.

Why it’s a turning point

The point is not that machines “think” — they don’t, in the human sense. The point is that for the first time anyone can delegate cognitive tasks — writing, summarizing, translating, coding — to a tool that answers in natural language, with no technical skills required. It is a shift comparable to the arrival of the personal computer or the internet: it doesn’t replace human intelligence, it multiplies its reach.