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

What does a 'context window' determine in an AI conversation?

The size of the screen where AI output is displayed The number of different topics an AI can discuss in a single session The time window during which a conversation is saved before being deleted How much text the AI can 'see' and refer to at once — including your messages, shared documents, and the AI's own replies. Text outside the window is effectively forgotten

What fundamentally changed about AI in late 2022 with ChatGPT's release, compared to AI that existed before?

AI was invented in 2022 — there was no meaningful AI before ChatGPT Generative AI crossed a quality and accessibility threshold — anyone could use it for genuinely useful work, without technical expertise, for the first time The 2022 breakthrough was AI becoming conscious and able to truly understand language ChatGPT was the first AI to run on computers; previous AI required specialized hardware

What is the 'basic prompt improvement loop'?

Copy your prompt from online prompt libraries rather than writing your own 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 Submit the same prompt three times and choose the best of the three responses

After completing AI Basics, what is the recommended next course in the AI Readiness curriculum?

The AI Economy course — which focuses on macroeconomic trends and is best taken after building practical AI skills There is no recommended next step — AI Basics is a standalone course with no further progression AI Landscape Essentials — which builds on this foundation to cover specific AI models, their capabilities, and how to choose between them for different use cases The Vibe Coding Economy course — which is the advanced technical course and assumes significant AI experience

Why does an AI model perform better on common tasks than on rare or highly specialized knowledge?

Common tasks appear more frequently in training data, so the model has seen more examples to learn from — rare or specialized topics have fewer training examples, increasing error and hallucination risk Rare topics require more parameters, and models have a fixed number available Common tasks are hard-coded into AI models; rare tasks require internet access AI models are intentionally limited on specialized topics to avoid replacing experts

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

AI is only about robots; machine learning is about software; generative AI is a marketing term 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 Generative AI came before machine learning, which came before AI

What is the '80/20 trap' in AI adoption?

AI handles 80% of a task well but fails at the remaining 20% in ways requiring human correction — making autonomous completion risky, but AI-assisted draft work highly valuable AI tools cost 80% more than traditional software and deliver only 20% of the value Only 20% of AI tools are reliable; 80% are experimental and should not be used at work AI can only be used for 20% of any professional's tasks; 80% must remain fully human

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 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 Using AI to replace human involvement in all written tasks entirely

When would you want a LOWER temperature setting for an AI task vs. a HIGHER temperature?

Lower for precise tasks (data extraction, code, factual answers) where consistency matters; higher for creative tasks (brainstorming, varied options, creative writing) where variation is desirable Temperature controls language formality: low for formal writing, high for casual writing Temperature only affects response speed, not content — lower is faster, higher is slower Higher temperature always produces better quality — you should maximize it for all tasks

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 AI 'hallucination'?

When AI generates plausible-sounding but factually incorrect content — stated confidently, without any signal to the user that the information may be wrong When AI produces visual artifacts or distorted images When AI becomes confused and stops generating responses When AI translates text incorrectly because it doesn't know the language

What is the core training objective of a large language model?

Create a database of question-answer pairs to look up when asked something Build a logical reasoning engine that can prove mathematical theorems Predict what word (token) comes next, given all the words before it — repeated billions of times across vast text to build pattern knowledge Memorize every fact in its training data so it can retrieve them accurately

Which of these everyday products uses machine learning or AI?

A standard calculator — it follows fixed mathematical rules with no learning A physical thermostat with a timer — it executes pre-set rules without learning A basic alarm clock — it does only what it's explicitly programmed to do Netflix's recommendation engine — it uses machine learning to predict what content you'll enjoy based on your viewing patterns

What is 'hallucination' in the context of AI?

Hallucination is a technical term for when AI models become overloaded and crash When AI becomes confused and stops responding to questions 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 made ChatGPT feel fundamentally different from previous AI products like Siri or Alexa?

ChatGPT was a general-purpose system — one interface could write, analyze, code, and answer questions across any domain, unlike assistants built for narrow command-response tasks ChatGPT was the first AI to use the internet in real time to answer questions ChatGPT was different because it ran on quantum computers unavailable to earlier AI ChatGPT was the first AI ever to understand English language

What does the Gartner Hype Cycle predict about technologies that go through a 'trough of disillusionment'?

The Hype Cycle predicts AI will be replaced by a completely different technology by 2027 Most eventually reach a 'plateau of productivity' where real, consistent value is delivered — the trough represents reality calibration, not permanent failure Technologies that enter the trough of disillusionment never recover and should be abandoned The trough of disillusionment only applies to hardware technologies, not software like AI

What happens when a conversation exceeds an AI's context window limit?

The AI stops responding and requires a new conversation to be started The AI loses access to text that falls outside the window — it effectively 'forgets' earlier parts of the conversation and may give inconsistent responses The AI stores the overflow in external memory and retrieves it when needed Exceeding the context window triggers an automatic upgrade to a larger model

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

A single character of text — AI processes text one character at a time A security credential required to access AI APIs 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 cryptocurrency used to pay for AI model usage

Which of the three habits of effective AI users is most important for working with AI in a professional context?

Keeping the judgment — AI generates options and drafts; the human remains accountable for decisions and final outputs Using AI for every task, no matter how simple, to build maximum fluency Never verifying AI output to avoid slowing down the productivity gains Delegating final decisions to AI to remove human bias from the process

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