AI Literacy
Understand what AI is, how it works, and where it fails — without writing a line of code.
- 1
Get oriented
- What Is AI, and How Do People Actually Use It? Forget the definitions. Here are the five things people actually do with AI every day, and what connects them. Entry
- What AI Can and Cannot Do (Yet) A capability map: what current AI is genuinely good at, what it fails at, and the dangerous middle category where it looks competent but isn't. Entry
- The AI Jargon Decoder Thirty AI terms, each in one sentence, each linked to a fuller explanation. Use it as an index when a word blocks you mid-article. Entry
- In What Order Should You Learn AI? A dependency-ordered path through AI concepts, from tokens to agents — plus three routes depending on how deep you actually need to go. Entry
- 2
How it actually works
- What Is a Token in AI? A token is the unit an AI model actually reads — not a word, not a letter. It explains your bill, your context limit, and why models miscount letters. Foundations
- What Is an LLM? A large language model is a machine trained to predict the next token. Here is why that simple objective produces something that looks like thinking. Foundations
- What Does "7B Parameters" Mean? Parameters are the adjustable numbers that hold everything a model learned. Why the count matters, and why it stopped being the whole story. Foundations
- How Does an LLM Actually Write? One token at a time, each conditioned on everything before it. The generation loop explains streaming, cost, and why models cannot revise. Foundations
- What Is an Embedding? Embeddings turn meaning into coordinates, which is what lets a machine compute with language. The foundation under search, RAG, and attention. Foundations
- 3
What it cannot do
- Why Do AI Models Hallucinate? Fluent, confident, wrong. Hallucination is not a bug in the system — it follows directly from what the system was built to do. Prompting
- What Is a Context Window? One token budget covering your prompt, the conversation history, retrieved documents, and the answer being generated. Everything competes for the same space. Prompting
- 4
Putting it to use
- Prompt Basics Not magic words. Prompting is task specification, and four reusable structures cover most of what works. Prompting
- What Is an AI Agent? Strip the hype and an agent is three things: a model that can call tools, a loop, and the authority to decide what happens next. Agents
- Why RAG Exists A model cannot know your documents and will confidently invent answers about them. RAG is the fix, and the problem is worth understanding before the solution. RAG
- Which Model Should You Use? Match the model tier to the task rather than defaulting to the largest. A framework that survives the next model release. Application
- 5
The landscape