Generative AI
Software that produces new text, images, audio or code in response to a request, by learning patterns from huge amounts of existing material. It drafts quickly, and you still judge the draft.
Also called: GenAI · AI chat assistants · chatbots
Connections
Leads on to
Ideas that build on this one.
Resources that teach it
- AI Fluency: Framework & Foundations (garden)
- Google AI Essentials (garden)
- Introduction to Generative AI (garden)
- LLM Course, Chapter 1: Transformer models (intro) (garden)
- OpenAI Academy: AI Foundations (garden)
- ChatGPT 101: A Guide to Your AI Super Assistant (recording) (garden)
- OpenAI Academy: Nonprofits club (garden)
- An AI glossary (garden)
- Co-Intelligence: Living and Working with AI (garden)
Competency
Find generative ai on the commons map ↗
You may know it from
- history the printing press making copies cheap When producing something becomes cheap, the scarce skill moves to choosing and judging what is worth producing.
In products
The same idea, under different names in different tools.
| Provider | What they call it |
|---|---|
| OpenAI | ChatGPT |
| Anthropic | Claude |
| Gemini | |
| Microsoft | Microsoft Copilot |
| Perplexity | Perplexity |
| xAI | Grok |
In plain words
Generative AI is the family of tools that make something new when you ask: a draft email, a summary of a long report, a picture, a slide outline, a few lines of code. They learned from enormous collections of human-made material, so they are good at producing things that look like what people usually write or make.
So what comes back is a plausible first draft. It can be excellent, generic or wrong, and it often looks equally confident in all three cases. Making a draft is now nearly free, so your time goes into deciding what you want and checking what you got.
Example
A volunteer coordinator needs a thank-you note for 40 event helpers. She asks an AI tool for three versions in different tones, picks the warmest one, adds two real details from the day, and sends it. The tool saved her from starting with a blank page, and she added the judgment and the details.
How it connects
- Most of today’s text tools run on a large language model, the engine underneath.
- What you get depends heavily on prompting and the context you give it.
- Because drafts can be confidently wrong, verification belongs in every workflow.
Try it
Pick something you need drafted and write one sentence describing what a good result would look like. Ask your AI tool for it, then hold its answer up against your sentence. Where did it match, and where did it drift?
Read this card as plain Markdown: /concepts/con-generative-ai.md. Know a parallel from your own field, or a product that implements this? Add it.