Objective: Introduce core AI concepts and how machines understand human language.

Topics Covered:

• What is AI? Types of AI (with examples like Siri, ChatGPT)
• AI vs. GenAI vs. AI Agents
• Intro to LLMs (in simple terms)
• Chatbot anatomy & workflows
• Hands-on: Play with ChatGPT (basic Q&A, fun facts, jokes)

Objective: Teach students to “talk” to AI and build first bots without code.

Topics Covered:

• Prompt anatomy: roles, context, instructions
  
  
• Chain-of-Thought prompting
  
  
• Persona prompts & formatting
  
  
• Flowise drag-and-drop flows
  
  
• Quick-start on Poe or Superagent

Project Idea: “Daily Motivation” or “Quiz Master” bot in Flowise

Objective: Teach basic Python programming to enable AI implementation.

Topics Covered:

• Python Basics: Variables, data types, operators
• Input/output (print, input functions)
• Control Structures: Conditionals (if-else) Loops (for, while)
• Functions & Lists: Defining and calling functions, Storing and manipulating data in lists
• Simple NLP: word count, keyword spotting

Project Idea: Vibe Checker” script: counts positive/negative words in a sentence

Objective: Combine Python logic with LLMs via API calls to build interactive agents.
Topics Covered:

• Understanding APIs

• • What are APIs?
  • How APIs Work
  • Why APIs are Useful for AI
• Installing & importing OpenAI or Hugging Face client libraries
• Sending prompts & receiving completions
• Dialogue loops & basic error handling
• Secure API key management (environment variables)

Project: “Fun Facts Bot” CLI: user asks a question → Python sends to LLM → prints a fun fact.

Objective: Explore how AI agents use memory and external knowledge with simplified LangChain and RAG concepts.

Topics Covered:

• Concept of memory: short-term vs. long-term
  
  
• Embeddings & vector stores (intuitive demos)
  
  
• RAG basics: retrieving docs, building context
  
  
• Connecting documents to bots for Q&A
  
  

Basic LLM pipelines (user → prompt → retrieval → response)

 Project: Build a “Notes Q&A Bot” that answers questions from your uploaded notes or a short document.

Objective: Build teams of AI agents using roles for collaborative problem solving
Topics Covered:

• Multi-agent systems & role definitions (Planner, Researcher, Writer)
• CrewAI setup & configuration
• Task coordination between agents 
• Ethics refresher: avoiding conflicting directives

Project: Build a “Project Team” of 3 agents: plan a school event or solve a challenge

Objective: Teach students how to build multi-step, automated workflows using AI and external apps, without writing code.

Topics Covered:

• What is workflow automation? Why use tools like n8n?

   

• Introduction to n8n: nodes, triggers, actions, and data flow

   

• Connecting n8n to ChatGPT using the OpenAI node

   

• Creating simple logic flows: conditions, loops, and delays

   

• Using triggers: Google Sheets, Webhooks, Time-based events

   

• Sending and receiving data between nodes

   

• Debugging workflows and managing failed executions

   

• Example use cases: auto-email responders, daily reminders, form processors

Project: “AI Workflow Assistant”
Students will build a multi-step workflow that uses AI to process input and trigger useful outputs A “Homework Help Bot” that takes a Google Form submission → sends it to ChatGPT → emails back a simplified explanation

Objective: Package your capstone agent into a portable container with a user interface.

Topics Covered:

• What is containerization? Why use Docker?
  
  
• Writing a Dockerfile for a Python/Streamlit (or Gradio) app
  
  
• Exposing ports & environment variables
  
  
• Building & running your container locally
  
  
• Basic UI design with Streamlit or Gradio components
  
  

Project:

1. Wrap your Phidata/CrewAI/LangChain agent in a Streamlit or Gradio UI.
   
   
2. Write a Dockerfile, build a local container image, and run it to verify functionality.

Objective: How to use GitHub for version control, project management, and deployment integration.

Topics Covered:

• What is Git and GitHub? Why developers use them
  
  
• Creating and cloning repositories
  
  
• Basic Git commands: add, commit, push, pull
  
  
• Using .gitignore and managing project files
  
  
• Writing good README files and commit messages
  
  
• Connecting GitHub to Vercel/Replit for deployment
  
  
• Understanding open-source and collaboration basic

Project Idea:
Push your AI assistant project to GitHub with a clear README

Objective: Deploy your containerized agent online so anyone can access it.

Topics Covered:

• Cloud vs. local apps: why cloud deployment matters
  
  
• Overview of Vercel
  
  
• Setting up a Vercel account and connecting GitHub
  
  
• Deploying your agent with serverless functions or static hosting
  
  
• Environment variables and deployment settings in Vercel
  
  
• Sharing live URLs and monitoring usage