What is Generative AI?
Also called: GenAI · Gen AI · Generative artificial intelligenceDefinition
Generative AI is a category of artificial intelligence that produces new content, including text, images, audio, video and code, in response to a prompt. It uses models trained on very large datasets to learn statistical patterns, then generates output that follows those patterns. Large language models such as those behind ChatGPT, Claude and Gemini are the best-known examples. Generative AI is powerful for drafting, summarising and analysis, but its output can be wrong or biased, so people must review it before use.
Most text-based generative AI runs on large language models that predict the next token of text given everything before it. Image and video generators commonly use diffusion models, which learn to turn random noise into a picture matching a description. Many current systems are multimodal, accepting and producing more than one type of content.
At work, generative AI is useful wherever people produce or process language: drafting emails and reports, summarising documents, writing code, preparing job descriptions, analysing feedback or creating first drafts of training material. The productivity gain comes from pairing the tool with a person who knows what good output looks like and checks it.
The main mistakes are trusting output without verification, pasting confidential or personal data into tools the company has not approved, and treating generative AI as a search engine with guaranteed facts. Organisations need a clear usage policy, approved tools, and training that teaches judgement as well as prompting.
Key points
- Creates new text, images, audio, video or code.
- Built on models trained on large datasets, such as LLMs and diffusion models.
- Output can be wrong, so human review is required.
- Needs an acceptable-use policy and approved tools at work.
An example at work
A marketing executive at a Mumbai consumer brand uses an approved generative AI tool to draft five versions of a product launch email, then edits the best one for tone and checks every product claim against the brief.
Where this is used at Bodhih
Related terms
Large language model
A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate language by predicting the next token.
Prompt engineering
Prompt engineering is the practice of writing and refining instructions to a generative AI model so it produces accurate, useful output for a task.
AI hallucination
An AI hallucination is output from a generative AI model that sounds confident and plausible but is false, unsupported or made up.
Responsible AI
Responsible AI is the practice of designing, deploying and using AI so that it is fair, safe, transparent, accountable and respects privacy.
AI literacy
AI literacy is the ability to understand what AI can and cannot do, use it effectively and safely, and judge the quality of its output.
Machine learning
Machine learning is a branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each rule.
More about Generative AI
What is the difference between AI and generative AI?
Artificial intelligence is the broad field of systems that perform tasks associated with human intelligence, including prediction, classification and recommendation. Generative AI is one branch that creates new content. A model that flags fraudulent transactions is AI but not generative; a model that drafts a customer email is generative AI.
Is it safe to use generative AI at work?
It can be, with safeguards. Use tools your organisation has approved, keep confidential and personal data out of unapproved tools, check facts and figures before relying on them, and follow your company’s AI policy. Responsibility for the final output stays with the person who uses it.