Generative AI: definition, how it works, tools

Generative AI covers technologies capable of producing original content, text, image, sound or code, from a model trained on large amounts of data. Since the public release of ChatGPT, this family of tools has spread into professional use, from marketing to code writing to image generation for business.

The term covers both a technology, the underlying language and diffusion models, and a category of uses, assisted content production at scale. Understanding this distinction helps pick the right tool for a given need: a text model to write, an image model to illustrate, each with its own strengths and limits.

What is generative AI?

Generative AI is a subset of artificial intelligence whose function is not to analyze existing data but to create new content that resembles it. A language model trained on billions of sentences becomes capable of composing an original and coherent text, rather than simply classifying or scoring content that already exists.

This family covers several types of models depending on the content produced: large language models (LLMs) for text, diffusion models for images, and specialized architectures for audio, video or computer code. Google’s AI Overviews, which compose a synthetic answer above search results, illustrate a direct application of this technology to information search.

The common trait across these different models is how they learn: they do not follow rules written by hand by a developer, but extract statistical patterns from examples. A language model learns grammar, style and part of its knowledge of the world by observing text, without ever being explicitly taught a grammar rule. This capacity to learn by example, rather than by programming, is what sets generative AI apart from traditional software.

How does generative AI work?

The basic principle of a generative text model is to estimate, from the start of a sentence, the most probable next word. By repeating this prediction over immense corpora, mostly drawn from the web, the model learns language patterns fine enough to compose answers that imitate human conversation.

The process relies on two distinct phases. During training, the model adjusts its internal parameters by observing massive amounts of text, images or sound. Once trained, it enters the inference phase: it generates an answer by composing, word by word or pixel by pixel, the most probable content given the request received and what it has learned.

This probabilistic logic explains both the tool’s power, able to handle very varied requests with no rule programmed in advance, and its limits: the model does not verify the truthfulness of what it produces, it reproduces a plausible statistical pattern.

Training these models requires considerable amounts of data and significant computing power, which explains why only a limited number of players, with the necessary infrastructure, currently publish the most widely used models. Once the model is available, however, day to day use, the inference phase, requires far fewer resources and can run through a simple web interface or mobile app.

The main generative AI tools

The market has structured itself around a few families of tools, each with its own dominant uses.

ChatGPT remains the reference for text generation and conversation, with a web search function that brings it closer to an answer engine. Gemini and Copilot rely on direct integration into existing office suites, Google Workspace for one, Microsoft 365 for the other, which eases adoption in business without changing work environment. Mistral AI positions itself as a European alternative, a criterion that matters for organizations under data sovereignty constraints.

For images, diffusion models like Midjourney start from random noise that they refine step by step until reaching a composition consistent with the text description provided. This family of tools has spread widely into creative uses, from illustration to advertising visuals. To compare conversational engines more precisely, our comparison of generative AI engines details their differences in use.

Generative AI use cases in marketing

In marketing, generative AI is first used to speed up content production: writing product descriptions, producing variants of the same ad message, generating visuals for social media, or drafting articles to be reworked afterward. It also helps personalize messages, adapting the same base content to different audience segments.

Generative AI also helps equip customer relations: chatbots able to rephrase a standard answer according to the tone of the conversation, or summarize a conversation history before handing it to a human agent. In e-commerce, it is used to generate product pages automatically from technical specifications, or to create variants of the same visual to test several ad formats.

A more recent use concerns brand visibility within the AI answers themselves. When a consumer asks ChatGPT a question instead of typing it into a classic search engine, being cited in the generated answer becomes a visibility issue in its own right. Our article on techniques to appear in ChatGPT’s answers details the concrete levers to achieve that.

In every case, generative AI remains a production and fast drafting tool: human validation, fact checking and adapting the output to the brand’s editorial line remain necessary before publication. A marketing team that folds it into its workflow mainly saves time on first drafts, not on final judgment.

Limits and risks of generative AI

The first limit concerns factual reliability: a generative model can produce a false statement with the same fluency as a true one, a phenomenon known as hallucination. No output should be published without review.

There are also questions of confidentiality, when internal data is entered into a prompt sent to a third party service, and of intellectual property, regarding the legal status of generated content and of the data used to train the models. An internal usage policy, specifying what can or cannot be submitted to these tools, limits most of these risks.

In Europe, the AI Act now regulates certain uses according to their risk level and imposes transparency obligations, in particular the requirement to disclose that content was AI generated. Businesses deploying these tools at scale must factor this regulatory dimension into their choices, alongside reliability and confidentiality concerns.

This same spread of generative AI into search habits also opens a new optimization ground for brands: generative engine optimization (GEO), which aims precisely at being cited by these models rather than merely being subject to them.

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