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

Which of the following describes a realistic and high-value use of AI in 2026?

Using AI to make final legal or medical decisions without a professional reviewing them Using AI to replace all written communication with a client without any human involvement Using AI to produce a first draft of a client proposal, then editing it with domain knowledge and relationship context the AI doesn't have Delegating an entire client project to an AI agent with no human review of outputs

Which of the following tasks is AI MOST reliable at?

Telling you whether a news article published yesterday is accurate — AI can't verify real-world current facts Producing a well-structured first draft of a professional email — AI draws on vast writing patterns and excels at fluent, structured text generation Accurately citing a specific legal case from last month — AI has knowledge cutoffs and hallucination risk with specific facts Calculating the exact compound interest on a complex loan schedule — mathematical precision is a known AI weakness

Which of the following is the correct relationship between AI, machine learning, and generative AI?

Generative AI is a type of machine learning, which is a type of AI — they are nested categories, not separate things Machine learning and generative AI are the same thing with different names AI is only about robots; machine learning is about software; generative AI is a marketing term Generative AI came before machine learning, which came before AI

What is 'calibration failure' in the context of AI hallucination?

When an AI model is calibrated to a different language than the user's When an AI's internal clock is set to the wrong time zone The process of adjusting an AI model to produce shorter responses The model's expressed confidence doesn't match its actual accuracy — AI states invented information in the same confident tone as verified facts, giving users no signal that something is wrong

What is 'hallucination' in the context of AI?

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

What is a 'token' in the context of AI language models?

A security credential required to access AI APIs A cryptocurrency used to pay for AI model usage A unit of text roughly corresponding to a word or word fragment — the basic unit AI models use to process and generate language, and how usage and pricing are measured A single character of text — AI processes text one character at a time

What is the 'basic prompt improvement loop'?

Copy your prompt from online prompt libraries rather than writing your own 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 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

What usage pattern consistently predicts high-value AI adoption?

Waiting for AI tools to mature further before integrating them into real workflows Trying every new AI tool as soon as it launches to stay current Using AI to replace human involvement in all written tasks entirely Starting with a specific, recurring real problem; using AI for first drafts with human review; building task-specific prompting habits; knowing which tasks to keep fully human

Why is 'Artificial Intelligence' considered a misleading name by many researchers?

AI is misleading because the system is not artificial — it uses real human data AI systems don't think or understand — they perform sophisticated pattern recognition and statistical prediction, which produces human-seeming outputs without human-like comprehension There is no controversy about the name — it accurately describes how the systems work The name is misleading because AI is more intelligent than humans in every domain

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