What is generative AI?
AI foundations, models and capabilities
Generative AI is a class of AI models that creates new content from input such as text, images, audio or documents. It can draft, summarise, translate and generate code, but it does not guarantee factual accuracy. In business use, its value usually comes from accelerating a first pass that people still review, refine and approve.
Reviewed by Jackie, Head of Learning & Development, Levellers - Last reviewed 8 June 2026
What this means
Generative AI is not the whole of AI. It is one class of AI models focused on producing derived synthetic content such as text, images, audio or video. Google also describes it as a class of models that creates content from user input.
In everyday work, that often means drafting, summarising, rewriting, translating, coding, captioning or creating image variants. So when someone says they are using AI to write first-pass customer replies or summarise a meeting pack, they usually mean generative AI rather than the full field of artificial intelligence.
Why it matters
Generative AI matters because it changes where people start. Instead of beginning with a blank page, a rough outline or a manual rewrite, teams can begin with a draft, summary or transformation and then improve it. That can be useful in communication-heavy, document-heavy and knowledge-heavy workflows where a decent first pass has real value.
It also matters because it is often mistaken for search, truth or judgement. It is none of those by default. A helpful draft can still be wrong, incomplete or unsuitable for the business context if the prompt, source material or review standard is weak.
How it works
The workflow usually starts with a prompt. Google defines a prompt as a natural language request sent to a generative AI model to elicit a response, and notes that prompts can contain text, images, video, audio, documents or multiple modalities. The model then generates an output based on the patterns it learned during training.
That training gives a model broad pattern knowledge, but not guaranteed access to the exact information your business needs today. Google notes that, to be useful in cases such as a product-aware customer service bot, generative models often need access to information outside their training data. That is why grounding, knowledge access and review matter so much in business use.
Examples
Useful examples include producing a draft response from a customer email, summarising a long meeting transcript, reformatting notes into a standard template, generating alternative product descriptions, extracting action points from documents or creating a first-pass explanation for a staff handbook. These are practical editing and transformation jobs, not magic.
Where the stakes are higher, the role of generative AI is usually assistive rather than final. It can help a person move faster, but the workflow still needs someone to check factual accuracy, tone, policy fit and whether the output should actually be used.
Common misunderstandings
A common misunderstanding is that generative AI is just chat. Chat is one interface, not the full category. The same underlying pattern can support text generation, code generation, image generation or multimodal tasks. If you want more on the model layer, see What is an AI model? and What is an LLM?.
Another misunderstanding is that a fluent answer is a verified answer. Google's glossary defines hallucination as plausible-seeming but factually incorrect output. That is why generative AI should not be treated as a substitute for evidence, source checking or formal approval in sensitive work.
Risks and boundaries
Generative AI models can produce output that is unexpected, offensive, inaccurate or poorly grounded. Google's generative AI overview notes the need for safety filters to block potentially harmful prompts and responses. NIST's generative AI profile also highlights intellectual property, privacy and harmful-content risks in generative AI systems.
For business use, the main boundary is simple: do not confuse a good first draft with a good final answer. Review is especially important where outputs could affect customers, money, legal position, staff decisions or regulated activity. Google's glossary explicitly treats human-in-the-loop review as a strategy for shaping, evaluating and refining model behaviour.
What to do next
Start with one repetitive writing or summarising task. Gather five to ten real examples, define what an acceptable output looks like, note what must never be invented or disclosed, and decide who reviews the result before use. That will tell you much more than a generic tool demo.
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FAQs
Is generative AI the same as AI?
No. Generative AI is one class of AI models. The wider AI field also includes models and systems built for prediction, detection, ranking, optimisation and other tasks.
Does generative AI only work with text?
No. Google documents inputs and outputs across text, images, audio, video, documents and multimodal combinations.
Can generative AI use company information?
Yes, but useful business outputs often require access to information outside the model's original training data, plus clear rules on grounding, privacy and review.
Can you publish generative AI output without review?
Sometimes for low-risk internal drafts, but not as a default rule. Hallucination, harmful output, copyright and privacy risks are real enough that review standards should reflect the stakes of the task.