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The vocabulary: AI, ML, deep learning, generative AI, LLMs, and agents
Distinguish the six core terms, artificial intelligence, machine learning, deep learning, generative AI, large language models, and agents, and how they nest inside each other.
- Time
- 20–25 min
- Type
- exercise
- Bloom
- Apply → Create
- XP
- 100
Architecture diagram for The vocabulary: AI, ML, deep learning, generative AI, LLMs, and agents. Nested-sets ladder: outer 'AI' ring containing 'ML' containing 'deep learning'; side overlays for 'generative AI', 'LLM', 'agent' with one icon-example each. Warm gold on near-black.
You'll be able to
- Distinguish the six core terms, artificial intelligence, machine learning, deep learning, generative AI, large language models, and agents, and how they nest inside each other.
- Use the classification ladder, a six-question test, to name the most precise label a system actually earns.
- Classify a system as agent or chatbot, so you can catch when someone uses the wrong word (especially calling a chatbot an agent) and name what capability is missing and why the gap changes risk and governance.
Key concepts · tap to reveal
1/15·Watch·Beat 1 · Hook
0%
Hook
Same product, five different names. The confusion isn't harmless, it's how you buy the wrong thing and argue past each other when it breaks.
Your task Write a prompt that asks Claude to recommend the right AI setup for a real task you're facing — then weigh its answer against this lesson, "The vocabulary: AI, ML, deep learning, generative AI, LLMs, and agents."
a strong prompt:role · context · task · format · example
Exercise · scenario
A spam filter that improves as users mark messages as junk.
Deliverable
Save the **Vocabulary Glossary** page in your AI Fluency Playbook so it's there when you need it. If it helps, write the six terms, AI, machine learning, deep learning, generative AI, LLMs, agents, in your own words with one example from your actual work, add the nested-circles sketch, and jot the one-line test you'll reach for: did a person write the rules, or did it learn from data; does it generate content; and can it take actions on its own? This isn't an assignment to hand in. It's a reference you'll flip back to.
Reveal model answer
Machine learning (non-generative)
Practice · Scenarios
0 of 2 revealed
Scenario 1 of 2
Given "book me a flight under $400," it searches sites and completes the purchase.
Quiz · adaptive · 3 items
Mastery check
Match each term to its definition. Pass at 80% to earn the lesson's XP and unlock the next.
Sources
- [1]Frontiers of Computer Science·A Survey of Large Language Models (2026) · Research
- [2]Briefings in Bioinformatics·Large language model agents for biological intelligence across genomics, proteomics, spatial biology, and biomedicine (2026) · Research
Submit your work for review
Paste your capstone artifact below. You'll get back a 4-level rubric grade, per-criterion feedback, and three concrete edits to strengthen it.