/applied-ai

Final Project Class Structure & Assignments

📚 Course Overview

Students will build an AI chatbot integrating WhatsApp, N8N, OpenAI, Supabase, and RAG, culminating in a live demo presentation. Each class includes theory, hands-on practice, and assignments.

📊 Evaluation & Grading Rubric

The final project is graded on 100 points, distributed as follows:

Stage Task Points Grading Criteria
Class 1 Define chatbot scope & set up infrastructure 10 pts Clear definition, proper setup of N8N & Supabase
Class 2 Connect WhatsApp API & process messages 15 pts Successful message reception & logging in Supabase
Class 3 Integrate OpenAI (GPT-4) for chatbot responses 15 pts AI-generated responses with well-structured prompts
Class 4 Implement Retrieval-Augmented Generation (RAG) 15 pts Proper knowledge base integration & query retrieval
Class 5 Store chat history & analyze conversations 15 pts Well-structured Supabase storage & insightful analytics
Class 6 Automate workflows using N8N 15 pts Successful chatbot automation & scheduled tasks
Class 7 Deploy & fine-tune chatbot 15 pts Optimized chatbot performance & deployment setup
Class 8 Live demo & final presentation 10 pts Clear explanation, working chatbot, innovation

✔ Late submissions: -2 points per day delay.
✔ Extra credit: Up to +5 pts for outstanding creativity or business impact.

🗓 Course Schedule & Assignments

Each class is 2 hours long, blending theory and hands-on activities.

🔹 Class 1: Defining the Chatbot Scope & Setting Up (10 pts)

🎯 Objective: Understand chatbot fundamentals & set up the project.

🛠 Hands-On:

  • Define chatbot purpose, audience & workflows.
  • Set up N8N, Supabase & obtain WhatsApp API credentials.

📌 Assignment:

  • Write a Chatbot Project Summary (business goal, features, users).
  • Install & configure N8N and Supabase.

🔹 Class 2: Connecting WhatsApp API & Processing Messages (15 pts)

🎯 Objective: Integrate WhatsApp API with N8N to receive messages.

🛠 Hands-On:

  • Set up WhatsApp API & test message reception.
  • Create an N8N workflow for WhatsApp message logging.

📌 Assignment:

  • Ensure WhatsApp API is fully configured.
  • Build an N8N workflow to log messages in Supabase.

🔹 Class 3: Integrating OpenAI for AI-Powered Responses (15 pts)

🎯 Objective: Use OpenAI (GPT-4) for chatbot responses.

🛠 Hands-On:

  • Set up OpenAI API & integrate it into N8N.
  • Experiment with prompt engineering to refine responses.

📌 Assignment:

  • Test different prompt styles to enhance chatbot responses.
  • Store chat history in Supabase for personalization.

🔹 Class 4: Implementing Retrieval-Augmented Generation (RAG) (15 pts)

🎯 Objective: Improve chatbot intelligence using knowledge retrieval.

🛠 Hands-On:

  • Set up a vector database for RAG.
  • Implement document embeddings & query processing.

📌 Assignment:

  • Upload sample knowledge base documents.
  • Test retrieval accuracy with sample queries.

🔹 Class 5: Storing Chat History & Analyzing Conversations (15 pts)

🎯 Objective: Store and analyze chatbot conversations in Supabase.

🛠 Hands-On:

  • Design a chat history table in Supabase.
  • Implement querying & analytics to improve interactions.

📌 Assignment:

  • Visualize chat statistics (most common queries, response time).
  • Generate insights on how to enhance chatbot interactions.

🔹 Class 6: Automating Workflows Using N8N (15 pts)

🎯 Objective: Automate chatbot interactions and tasks.

🛠 Hands-On:

  • Create automated chatbot actions based on user intent.
  • Implement scheduled tasks (e.g., follow-ups, reminders).

📌 Assignment:

  • Develop an automated response flow for common user interactions.
  • Configure alerts for unanswered queries.

🔹 Class 7: Deploying & Fine-Tuning the Chatbot (15 pts)

🎯 Objective: Deploy the chatbot for real-world use & optimize performance.

🛠 Hands-On:

  • Deploy chatbot for external testing.
  • Optimize response speed and database queries.

📌 Assignment:

  • Collect user feedback & iterate chatbot improvements.
  • Ensure chatbot is ready for final demo.

🔹 Class 8: Live Demo & Final Presentation (10 pts)

🎯 Objective: Showcase chatbot functionality & key learnings.

🛠 Hands-On:

  • Each student/team presents their chatbot.
  • Showcase real-time interaction & workflow automation.

📌 Final Submission:

  • Final report (architecture, features, challenges, improvements).
  • GitHub repository with chatbot code.
  • Live demo evaluation based on chatbot functionality & creativity.

🏆 Final Deliverables

By the end of the course, students will have a fully functional AI chatbot integrated with WhatsApp, OpenAI, Supabase, and N8N, capable of retrieving information via RAG, storing conversations, and automating interactions.

Each student/team will pitch their chatbot and demonstrate it working live.

✔ Bonus: Up to +5 pts for exceptional performance, innovation, or UX design.