• Content Outline

  Evolving (high-level) outline of the content we envision for our 42 days of AI.

• Day 1: Dev Environment Setup

  • VSCode configuration for Python • Essential VSCode extensions • Python libraries • Install AI-focused libraries • Introduction to Jupyter notebooks and embedding that into VSCode • NOTE: Even if you are an experienced developer, you will benefit from the VSCode and AI libraries details • SIDEBAR: Preview of the Capstone project • RESOURCES: References to dive deeper on today's topics/tools.

• Day 2: First AI App

  • Validate the Python/AI libraries • 'Hello AI', our first program using AI APIs. • SIDEBAR: Quick tour of the current state-of-the-art of the AI tooling landscape. • RESOURCES: Links to related articles/videos.

• Day 3: AI Prompt Engineering, Part 1

  • Prompt Engineering: Getting the LLM to do what you want. • Multiple examples of best and worst prompt practices (using ChatGPT free). • SIDEBAR: We didn't used to know how to spell "prompt engineer." Now we are one. • RESOURCES: Prompt engineering/vibe coding "greates hits" articles list.

• Day 4: AI Prompt Engineering, Part 2

  • Invoke a LLM using a chat-like prompt via its API. (Example: 'How would you go about designing a program that keeps itself updated as the underlying technology evolves.') • This will be the outline we use to have AI help us build this. • SIDEBAR: Local/open-source LLMs vs. commercial APIs. Things to think about for when we deploy (and need to pay for running) our AI applications in the real world.