MegaMaester

Artificial Intelligence · Module 3

Generative AI

Module 1 explained what AI is; Module 2 opened the box on machine learning. This module covers the technology behind the tools that put AI in everyone's hands: generative AI — systems that create text, images, audio, and code rather than only classifying or predicting.

You will learn what generative AI actually is, how large language models work (they predict likely text, they do not understand it), how to prompt them well, where they are genuinely useful and where they fail, how image and multimodal generation works, how to use these tools responsibly, and how to stay grounded amid the hype.

Lessons
7 lessons
Estimated time
~6-8 hours
Assessment
Module quiz included

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Lessons

Lesson 1 · 16 min

What Is Generative AI?

Lesson 2 · 16 min

How Large Language Models Work

Lesson 3 · 16 min

Prompting: Getting Good Results

Lesson 4 · 16 min

Strengths and Limits of Generative AI

Lesson 5 · 16 min

Images, Audio, and Multimodal AI

Lesson 6 · 16 min

Using Generative AI Responsibly

Lesson 7 · 17 min

Generative AI and the Near Future

Check your understanding

Finish the lessons, then test yourself. Your best score is saved on this device. You can also open this quiz on its own page.

Generative AI — Module Assessment

14 questions · pass mark 75%

  1. 1.What most clearly distinguishes a generative model from a discriminative one?
  2. 2.Which best describes how a generative model produces a novel output?
  3. 3.What is a large language model fundamentally trained to do?
  4. 4.Why can a large language model produce fluent output that is completely false (a hallucination)?
  5. 5.Why does a vague prompt like "Write about climate change" tend to produce a generic answer?
  6. 6.Which statement best captures what 'prompting' is?
  7. 7.What does it mean when a generative model 'hallucinates'?
  8. 8.Which task is generative AI most reliably suited to?
  9. 9.In plain terms, how does a diffusion-based image generator produce a picture from a prompt?
  10. 10.Why is checking provenance a better defense against deepfakes than trying to spot fakes by eye?
  11. 11.An AI gives you three well-formatted book citations for an essay. What is the responsible next step?
  12. 12.Which situation best fits keeping a human responsible rather than letting a generative tool decide?
  13. 13.What best captures a calibrated view of generative AI's recent progress?
  14. 14.Why are the earlier 'AI winters' relevant to how you read today's AI claims?
Answer every question to submit.