Keywords bank
Every term the course defines, in plain language. Grows as more lessons land.
- Bias
- AI output reflects the average of its training data, including its skews. Average is not the same as good, fair, or true.
- Confidence ≠ correctness
- The key safety rule: how sure an AI sounds tells you nothing about whether it's right. Fluency is not evidence.
- Context
- The background you give the AI about your situation — who it's for, what you have, the constraints. It can't see any of this unless you say it.
- Deterministic
- Always giving the exact same output for the same input (like a calculator). An LLM is NOT deterministic — ask the same thing twice and the wording differs.
- Few-shot (example-giving)
- Showing the AI one or two examples of what you want. One example is worth a paragraph of description, because pattern-matching is what it does best.
- Hallucination
- When an AI gives a confident answer that is plausible-sounding but actually wrong, because it generates likely text rather than retrieving verified facts.
- Iteration
- Refining in the same conversation ('warmer, less jargon') instead of restarting. The conversation itself is the tool.
- Large language model (LLM)
- The technology behind tools like ChatGPT and Claude. A system trained on a huge amount of text that works by predicting the most likely next word.
- Next-word prediction
- The core mechanism of an LLM: given some text, it produces the most plausible continuation, one piece at a time. Reasoning-like behaviour emerges from doing this extremely well.
- Prompt
- What you send the AI. Not a search query — think of it as briefing a collaborator. Its quality sets the ceiling on the output.
- Role prompting
- Telling the AI who to be ('you are a patient tax accountant…'). It steers the answer toward the relevant region of what the model learned.
- Training cutoff
- The date an AI's knowledge stops. Plain chat has no live internet, clock, or calculator — so 'recent' or 'today' questions are unreliable without tools.
- Training data
- The body of text an AI learned from, up to a fixed 'cutoff' date. Out of the box it knows nothing after that date unless a tool lets it search.
- Verify
- Independently checking an AI's factual claims (and that its sources exist) before relying on them — a routine habit, not an afterthought.
taught in: What AI can't (and shouldn't) do · L0see also: Hallucination, Verify
taught in: Talking to AI: prompting that works · L0see also: Prompt
taught in: What an AI model actually is · L0see also: Next-word prediction
taught in: Talking to AI: prompting that works · L0see also: Prompt
taught in: What an AI model actually is · L0see also: Next-word prediction, Training data
taught in: What an AI model actually is · L0see also: Large language model (LLM)
taught in: Talking to AI: prompting that works · L0see also: Prompt
taught in: What AI can't (and shouldn't) do · L0see also: Confidence ≠ correctness