What is generative AI

Generative AI defined for marketers

For marketers, generative AI is both a content tool and a search channel. Most teams already use the first side. You ask a model for an ad headline or a product description, then edit what it gives you. The second side matters more for visibility, because assistants like ChatGPT and Perplexity now answer buyer questions directly and name the brands they trust. Check how your team uses these tools and whether the tools mention you.

How generative AI works

Generative AI works by predicting the next piece of output from patterns it learned during training. A large language model (LLM) reads billions of words and stores the statistical links between them. When you type a prompt, the model picks a likely next word and repeats that step until the answer is complete. Image models do the same kind of thing with pixels. Some assistants also search the web before they answer, a method called retrieval-augmented generation (RAG). A model without search only knows what it saw before its training cutoff, so check whether your assistant searches the web.

Generative AI vs traditional AI

Traditional AI sorts or predicts from existing data, while generative AI produces something new. A spam filter decides whether an email is junk. A recommendation engine ranks products you might buy, but it never writes the product page. A generative model writes the email or the page itself. In practice the line blurs, since Google's AI Overviews rank sources and then generate a summary from them. Traditional AI produces labels or scores, while generative AI produces new content.

Where generative AI is used

Generative AI shows up wherever a person needs a first draft or a quick answer. Marketing teams met it first in content work, but it has spread well past that. Today it's common in these areas:

Why some brands get cited

Generative AI cites brands whose information it can find and trust. No assistant publishes its formula, and each one weighs signals in its own way. Assistants favor authority across trusted sources and content they can quote.

Authority across trusted sources

Models favor brands with repeated mentions in sources they already rely on, such as reviews or news coverage. Wikipedia and industry lists are other examples of these sources. If your product facts match across those sources, the model has more reason to repeat them. When the details conflict, the model hedges or names a competitor instead.

Content the model can quote

An assistant that searches the web pulls passages that answer a question in a sentence or two. A page that opens with a clear definition and keeps to one topic per section is easier to lift than one buried in sales copy. Crawlers also need access. A site that blocks AI bots or loads its text through scripts can't be cited at all.

Get cited by AI

To get cited by AI, make your brand easy to find and easy to quote, then measure what happens. First, ask ChatGPT and Perplexity the questions your buyers ask, and write down which brands they name. Then rewrite your key pages so each one answers its question in the opening lines. After that, work on getting mentioned on the third-party sites the assistants already cite. Run the same check every month, because answers change when models update.

Generative AI in simple terms

In simple terms, generative AI is software that has read huge amounts of text and images and now makes new ones on request. It works like the autocomplete on your phone, only trained on far more data and able to write whole pages. It produces what sounds likely, which is why it can sound sure of itself and still be wrong. Check every claim before you publish it.

Related terms to know

Start with LLM and RAG if you're still working out what is generative AI under the hood. Move to GEO and AEO once your question turns to visibility. Keep hallucination in mind at every stage, because it's the reason a person should review anything a model writes.

For the model mechanics, read how LLMs work; for Google's first generative search product, see Search Generative Experience.