Start here · Essential guide
Generative AI Explained for Beginners
A plain-language definition of generative AI, how these tools learn from examples, what they do well, where they fall short, and how to use them sensibly.
AI and Work
AI Tools Explained
How generative AI tools work, where they fail and how to use them carefully.
AI Bias Explained With Everyday Examples
See how skewed or incomplete training data leads to skewed results, using simple hypothetical examples, plus questions worth asking about any AI system.
AI Image Generators: How They Work and Their Limits
A high-level look at how AI image generators turn text into pictures, plus the typical failures, such as odd hands, garbled text and ignored instructions.
Checking AI Output Before You Rely on It
A practical routine for fact-checking AI-generated text: what to verify first, how to trace claims to sources, and when to bring in a qualified person.
Deepfakes and Synthetic Media: How to Think About Them
What deepfakes and synthetic media are, why they can be convincing, and sensible habits for judging images, audio and video without becoming paranoid.
How Chatbots Produce Answers and Why They Make Mistakes
Chatbots generate text by predicting likely wording, not by looking up verified facts. Learn why that produces confident, fluent answers that are sometimes wrong.
On-Device AI and Voice Assistants
Compare AI that runs on your own device with AI that runs in the cloud, and what that means for voice assistants, speed, privacy, battery life and capability.
Privacy When Using AI Assistants
What not to type into AI assistants, how to review data and history settings, and simple habits that reduce privacy risk without giving up useful tools.
Writing Better Prompts: Clear Instructions for AI Tools
How context, constraints, examples and follow-up questions improve results from AI tools, with before-and-after prompts and a reminder to still check the output.
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