AI
Artificial intelligence, explained in plain English
Plain English guides to AI: how chatbots and image tools work, where they get things wrong, what happens to your data, and how to use them safely.
Artificial intelligence is the only technology in years that arrived in everybody's life before anybody explained it. These pages do the explaining. They start from what a model actually is, which is software that got its behavior from patterns in data rather than from rules somebody typed, and work outward to the things you notice in daily use: why a chatbot invents a source, why it forgets the beginning of a long conversation, why it agrees with you when you are wrong.
If you are new to the subject, read the plain English definition first, then how a language model produces an answer. If you already use these tools daily, the two hubs that change results fastest are prompting and knowing where AI goes wrong. If your concern is what happens to what you type, start with AI and your privacy, and if it is your job, AI at work covers policies, contracts and the tasks where it genuinely pays.
AI basics: what it is and how it got here
The ground floor: what AI is, how it learns, and the vocabulary you need before anything else makes sense.
- What is artificial intelligence? A plain English definition
- Machine learning explained: how software learns from examples
- Neural networks explained: what is actually inside a model
- Where AI training data comes from and why it matters
- What AGI means, and why smart people disagree about it
- All 6 articles in aI basics: what it is and how it got here
How language models work, from tokens to reasoning
The machinery behind every chatbot, explained so the odd behaviour you see starts to make sense.
- How a language model actually works: tokens and probability
- What tokens are and why they decide your AI bill
- Context windows: why an AI forgets the start of a long chat
- How an AI model is trained, from raw text to chatbot
- Reasoning models: what thinking before answering means
- All 10 articles in how language models work, from tokens to reasoning
Prompting: how to ask an AI and get a useful answer
Better questions, better answers: the habits that turn a vague chatbot reply into something you can use.
- How to write a prompt that gets a usable answer
- Few shot prompting: giving the AI examples that work
- How to make an AI return the exact format you asked for
- Prompt chaining: splitting a big task into steps that work
- System prompts: the instructions you never see
- All 7 articles in prompting: how to ask an AI and get a useful answer
When AI gets it wrong: hallucinations, bias and limits
AI is wrong often enough to matter. Here is why it happens and how to catch it before it costs you.
- Why AI makes things up, and what a hallucination really is
- Why AI sounds certain when it is wrong
- Why an AI agrees with you even when you are wrong
- Made up sources: why AI invents books, cases and studies
- Knowledge cutoffs: why your AI does not know last week
- All 9 articles in when AI gets it wrong: hallucinations, bias and limits
AI images, video and voice: how they are made
The creative side of AI and its dark twin: the same tools that make art also make convincing fakes.
How to spot AI generated text, images and video
What the tells really are, why detector scores are not evidence, and what provenance data can prove.
AI and your privacy: data, training and opting out
Everything you type is data somewhere. Here is who holds it and which switches actually turn it off.
AI at work: policies, tools and what to watch
Where AI genuinely helps at work, where it quietly creates liability, and what to agree before you roll it out.
- AI at work: what it is really used for right now
- Where AI actually saves time at work, and where it costs you
- Writing an AI policy your team will actually follow
- AI meeting notes: what to check before you trust the summary
- AI in hiring: what the screening software really does
- All 8 articles in aI at work: policies, tools and what to watch
AI tools compared: free versus paid, search and phones
Straight comparisons so you can pick a tool, or decide you do not need to pay for one at all.
AI and the law: the rules that reach ordinary users
What the new AI rules actually require, written for the people who use these tools rather than the lawyers who draft the policies.
AI in everyday life: study, applications and family
Using AI for the things people really use it for: coursework, job applications and children who already found it first.