MegaMaester

Artificial Intelligence · Module 7

Building with AI

The first six modules explained how AI works, where it is used, and the ethics around it. This module is about doing: turning AI from something you use into something you build with. You do not need to be a programmer to follow along — the goal is to understand the building blocks and the path from idea to working tool.

You will explore the builder's toolkit, how software talks to AI through APIs, no-code and low-code platforms, how to ground AI in your own trusted data, AI agents and automation, how to test and evaluate AI products, and how to take an idea from a rough prototype to something people can actually rely on.

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

Not started yet

Lessons

Lesson 1 · 16 min

From User to Builder: The AI Toolkit

Lesson 2 · 16 min

Working with AI Models and APIs

Lesson 3 · 16 min

No-Code and Low-Code AI

Lesson 4 · 16 min

Grounding AI in Your Own Data

Lesson 5 · 16 min

AI Agents and Automation

Lesson 6 · 16 min

Testing, Evaluating, and Improving AI Products

Lesson 7 · 16 min

From Prototype to Product

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.

Building with AI — Module Assessment

14 questions · pass mark 75%

  1. 1.Which statement best captures the shift from using AI to building with it?
  2. 2.What does Hugging Face primarily provide to builders?
  3. 3.In an API call to a model, what does a 'request' consist of?
  4. 4.Why might an organisation self-host an open-weight model like Meta's Llama instead of calling a closed API?
  5. 5.What is the defining promise of no-code and low-code platforms?
  6. 6.Which situation is the strongest reason to choose custom code over a no-code platform?
  7. 7.In retrieval-augmented generation, what happens before the model generates its answer?
  8. 8.Which statement about grounding AI in your own data is accurate?
  9. 9.What most distinguishes an AI agent from a single prompt-and-response?
  10. 10.Why are long autonomous agent chains often unreliable even when each single step is fairly good?
  11. 11.What makes evaluating AI outputs harder than testing ordinary deterministic software?
  12. 12.What best describes red-teaming an AI system?
  13. 13.Why is a polished AI demo a poor indicator that you have a finished product?
  14. 14.A beginner wants to ship an AI feature responsibly for the first time. Which approach best reflects this lesson?
Answer every question to submit.