AI Search 101
What is generative AI?
Generative AI is artificial intelligence that produces new content rather than classifying or predicting from existing data. Trained on large collections of text, images, audio or code, these systems learn the structure of the material well enough to create fresh examples of it from a written instruction.
The basics
| Also called | GenAI |
| Produces | Text, images, audio, video, code |
| Main model types | Large language models for text, diffusion models for images |
| Built on | Deep learning and neural networks |
| Controlled through | Prompt engineering |
| Examples | ChatGPT, Claude, Midjourney, Stable Diffusion, ElevenLabs |
How is generative AI different from other AI?
Most artificial intelligence in commercial use before 2022 was discriminative. It sorted things: this email is spam, this transaction is fraudulent, this customer is likely to churn. The output was a label or a number, and the system was built for one task.
Generative systems produce an artefact instead. Asked for a paragraph, an image or a function, they return one that did not previously exist. The difference is not only in the output but in the range: one generative model handles tasks it was never specifically built for, because generating plausible continuations turns out to cover an enormous amount of useful work.
That breadth is what made the technology a general business question rather than a specialist one. A fraud model concerns the fraud team. A model that writes, summarises, translates and codes concerns everyone.
How does generative AI actually generate?
The mechanism differs by medium, and the two dominant approaches are worth distinguishing.
Text is generated sequentially. The model predicts the next fragment, appends it, and predicts again, each step conditioned on everything written so far. Fluency comes from the fact that every token is chosen in the context of all the others.
Images are generated by diffusion. The model starts from random noise and removes it in steps, at each stage nudging the result toward something matching the description. Having learned to reverse the process of adding noise to real images, it can run that reversal from pure noise into a new image.
Both share the same underlying bargain. The model has absorbed the statistical structure of an enormous body of examples, and it produces work that is consistent with that structure. This is also the source of its characteristic weakness: output tends toward the average of what it learned unless it is pushed somewhere specific.
What is generative AI genuinely good at?
Three things, reliably. Drafting, where getting from nothing to something is the expensive step and editing is cheap. Transformation, where material already exists and needs to become a different shape, a different length or a different language. And variation, where twenty options are more useful than one.
It is weaker wherever correctness cannot be checked cheaply by the person using it. Generating a paragraph about a familiar topic is safe because errors are visible. Generating a statistic, a citation or a legal clause is not, because the output looks equally confident either way.
What should a business be careful about?
Accuracy. Fluent text is not verified text. Anything factual needs checking against a source, and the checking has to be someone's actual job rather than an assumption.
Confidentiality. Consumer tiers of these tools may use submitted content to improve models. Business tiers and APIs generally do not. Knowing which tier a team is on matters more than any policy document.
Rights. Training data provenance varies and so does the licence attached to output. For anything published commercially, particularly images, the terms are worth reading before rather than after.
Sameness. A model trained on the average produces the average. Teams that use it for volume tend to produce material that describes them the way it describes everyone else, which is a marketing problem long before it is a technical one.
How generative AI is used in marketing
Production. Drafting and adapting copy, generating visual concepts, producing variants for testing, and translating between languages. The most heavily adopted use by a wide margin.
Analysis. Summarising research, clustering open-text survey responses, classifying enquiries, and reading long documents for the parts that matter.
Distribution. The same models now sit between a company and its buyers, answering questions about categories and vendors. What a model says about a company has become a distribution question rather than a production one.
How this impacts your business
A language model does not look a company up in a register. It composes a description from everything it read during training and whatever it retrieves at the moment of the question. Trade press, directories, forums, competitor comparison pages, and somewhere in that mix the company's own website too.
So the description a buyer sees is assembled, not published. No company controls it, but every company supplies a share of the material it is built from.
The traffic impact is still small, roughly one percent of sessions for most companies. The influence is not small, because the reader is often a buyer forming a first impression of what a company does and who it serves, before any page gets a click.
Checking takes ten seconds. Type the company name into ChatGPT and read the answer.
Frequently asked questions
Is generative AI the same as a large language model?
No. A large language model is one kind of generative system, specialised in text. Image, audio and video generators are generative AI built on different architectures.
Does generative AI copy its training data?
Not ordinarily. It produces new combinations consistent with patterns it learned. Near-verbatim reproduction can happen where a passage was highly repeated in training, which is rare and is treated as a defect.
Can generative output be detected?
Not reliably. Detection tools produce both false positives and false negatives at rates that make them unsafe for consequential decisions.
Who owns what generative AI produces?
It depends on the tool's terms and the jurisdiction. Several jurisdictions decline copyright protection to works lacking human authorship, and provider terms differ on commercial use.
Where should a company start?
With a task where the output is checked by someone who would notice an error. That constraint rules out most of the uses that go wrong.
Related definitions
- What is a large language model?
- What is ChatGPT?
- What is prompt engineering?
- What is artificial intelligence?
- AI Search 101
Written by Lari Numminen, Generate More. Also available in Finnish. Updated 31 August 2026.