4-Week Practical Course: Application Programming with GEN-AI and Python

Week 1: Python Foundations & Environment Setup

Day 1: Course Introduction & Environment Setup

• Overview of the course, expectations, and project goals.
• Installing Python, setting up virtual environments (venv/Conda), and using IDEs (VSCode/Jupyter Notebooks).
• Running simple “Hello, World!” scripts.

Day 2: Python Essentials for GEN-AI

• Python basics: variables, data types, control structures, functions, error handling.
• Writing clean, modular code for application development.
• Lab: Build small utility functions for text manipulation.

Day 3: Working with Libraries & Data

• Python libraries: Requests (HTTP API calls), JSON (parsing API responses), Numpy/Pandas (basic data handling).
• Lab: Fetch and process JSON data from a public API.

Day 4: Introduction to APIs & Basic Application Building

• Understanding APIs and their role in GEN-AI applications.
• Lab: Build a command-line app to retrieve and display data from an API.

Week 2: Introduction to Generative AI Tools & Concepts

Day 1: What Is Generative AI?

• Overview: history, breakthroughs, trends, ethical considerations.
• Demo: Showcase text and image generation AI tools.

Day 2: Hands-On with Hugging Face Transformers

• Introduction to Hugging Face ecosystem and pre-trained models.
• Lab: Load a text generation model and experiment with parameters.

Day 3: Working with OpenAI’s GPT APIs

• Setting up an API key and understanding OpenAI API documentation.
• Lab: Write a script that sends a prompt and processes the response.

Day 4: Building a Simple Chatbot

• Design a basic chatbot using Hugging Face/OpenAI GPT models.
• Lab: Develop and refine chatbot responses through prompt engineering.

Week 3: Developing Full-Featured GEN-AI Applications

Day 1: Application Design & Use Cases

• Identifying real-world use cases: chatbots, content creation, summarization.
• Introduction to RAG
• Activity: Brainstorm final project ideas.

Day 2: Integrating GEN-AI with Python Web Frameworks

• Tools: Gradio and Streamlit for UI development.
• Lab: Build a simple web app interface for the chatbot.

Day 3: Exploring Other GEN-AI Modalities

• Overview of image generation APIs (DALL-E, Stable Diffusion).
• Lab: Create a mini-app that retrieves AI-generated images.

Day 4: Deep Dive into Prompt Engineering

• Techniques for effective prompts and iterative refinement.
• Lab: Optimize chatbot responses through prompt tuning.

Week 4: RAG Project & Advanced Topics

Day 1: Building a Simple RAG Chatbot with Haystack

• Understanding document stores, retrievers, and readers in Haystack.
• Lab: Implement a basic RAG chatbot that retrieves relevant data and generates responses.

Day 2: Application Design & Use Cases

• Identifying real-world use cases: knowledge retrieval, enterprise search, AI-assisted Q&A systems.
• Activity: Brainstorm final project ideas.

Day 3: Implementing a Vector Database

• Introduction to vector databases (FAISS, Weaviate, or Pinecone).
• Lab: Store and retrieve embeddings for documents using Haystack.

Day 4: Project Presentations & Wrap-Up

• Students showcase their final projects.
• Q&A session, feedback, and discussion on future learning paths in GEN-AI.