What is generative AI?
By Yassine Seghrouchni, founderUpdated
In short
Generative AI is AI that creates new content: text, images, audio, video or code. You give an instruction in plain language, the prompt, and the system produces something that didn’t exist before. ChatGPT, Claude, Gemini and Copilot are well-known examples. The difference from other AI: that sorts or predicts, while generative AI makes something new.
Also known as: GenAI
How it works
A generative model has been trained on huge amounts of existing text, images or code and has learned patterns from them. A large language model predicts, word by word, what logically comes next. An image model builds up a picture that fits the description.
The model doesn’t look anything up or copy an existing answer. It produces what is likely, given your instruction. So the output is new, but unchecked. A text can be fluent and convincing and still contain a mistake.
Where it saves businesses time
Generative AI is strongest in work where someone produces a first version that is then reviewed:
- Drafts: a quote, a reply to a customer question or a job advert. See creating quotes with AI.
- Summaries: a meeting, a long document or an email thread. See AI meeting minutes.
- Pulling data out of documents: the fields on an invoice or the terms in a contract.
- Rewriting and translating: the same text for a different audience or in another language.
It works best when the model gets your own information, such as previous quotes, your price list or your handbook. That approach is called RAG.
Where it goes wrong
Three risks keep coming back. The model sometimes makes something up that sounds convincing: a hallucination. Staff paste confidential data into a free account. And generated text or images go out without anyone checking them.
Behind the first two lies the same misconception: that generative AI is a knowledge base that knows the answer. It’s a text generator. Treat the output as the draft of a new colleague who writes quickly and convincingly but doesn’t check their sources.
Why it matters in practice
Start with a task where a person already reviews the output before it goes out. AI writes the draft, a member of staff checks and sends it. That way you get the time saving without a mistake slipping through to a customer.
Sort out the basics too: a business account instead of personal ones, a short agreement on which data may go in, and when you mention that something was made with AI. How to set that up is covered in ChatGPT for business and writing an AI policy.
Frequently asked questions
What is the difference between generative AI and an LLM?
Generative AI is the umbrella term for AI that creates new content, from text to images, audio and video. A large language model (LLM) is the kind of generative model that produces text. ChatGPT and Claude run on an LLM. An image generator is also generative AI, but not an LLM.
Do you have to say that something was made with AI?
In certain cases, yes. The AI Act requires transparency, for example when people talk to a chatbot, or with AI-generated images that look like the real thing (Article 50). Set out in your AI policy when you disclose it, so staff don’t have to decide that for themselves each time.
