AI Basics Quiz: What Do You Actually Know About AI?

What is 'hallucination' in the context of AI?

When AI becomes confused and stops responding to questions Hallucination is a technical term for when AI models become overloaded and crash When AI generates plausible-sounding but factually incorrect information — because the model predicts likely text, not verified facts When AI produces visual hallucinations or images instead of text

What is the 'basic prompt improvement loop'?

Send a prompt, notice what's missing or wrong in the response, add that context and re-prompt, repeat — treating AI interaction as iterative rather than single-shot Submit the same prompt three times and choose the best of the three responses Always delete and restart a new conversation if the first response isn't perfect Copy your prompt from online prompt libraries rather than writing your own

Which of the following is an example of AI that predates ChatGPT and has been running invisibly for years?

A basic spreadsheet formula that calculates the sum of a column A dropdown menu that shows you your most recently visited websites ChatGPT Plus, which was the first commercial AI product available to consumers A credit card fraud detection system that flags unusual transaction patterns — a classic machine learning application deployed by banks for over a decade

A lawyer asks an AI for case citations supporting a specific legal argument. What is the risk, and what should they do?

High hallucination risk — AI may generate convincing-looking but nonexistent case citations. Every citation must be verified in a legal database before use There is no risk — AI was trained on legal databases and citations are reliably accurate The risk is only if the lawyer is in a jurisdiction not covered by US law training data The risk is low if the AI is asked to cite recent cases from 2024 or later

Even the most capable AI models in 2026 cannot do which of the following?

Produce grammatically correct text in English Summarize a long document into key bullet points Write code that executes correctly in common programming languages Reliably verify whether their own output is factually correct — models can produce false information with the same confidence as true information