AI+ Vibe Coder™

Supercharge coding with AI+ Vibe Coder™ for smarter, faster creation

• Beginner-Friendly Approach: Designed for aspiring creators eager to explore AI-assisted coding with ease and confidence
• Interactive Learning Journey: Blends core coding concepts, intuitive AI tools, and hands-on practice to build real problem-solving skills
• Project-Driven Growth: Provides guided exercises and practical projects to help you build, refine, and showcase your AI-powered coding talents

Module 1: Introduction to Vibe Coding & AI Tools

• 1.1 What is Vibe Coding?
• 1.2 Evolution of AI in Software Development – Low Code vs No Code vs Vibe Coding
• 1.3 Overview of Common AI Coding Tools by Functionality
• 1.4 SDLC for a Vibe Coding Product
• 1.5 Hands-on Lab: Familiarizing Learners with Multiple AI Coding Tools
• 1.6 Case Studies

Module 2: Prompting for Code – Basics & Best Practices

• 2.1 Anatomy of a Good Prompt
• 2.2 Prompt Types – Instructive, Descriptive, Iterative
• 2.3 Prompting Patterns – Zero-Shot, Few-Shot, Chain-of-Thought
• 2.4 Hands-on Lab: Practice Zero-Shot, Few-Shot, and Chain-of-Thought Prompting
• 2.5 Use-Case 1: Creating a Python Calculator
• 2.6 Use-Case 2: Optimizing AI-generated Code Using Different Prompt Types

Module 3: Debugging & Testing via AI

• 3.1 Reviewing and Refining AI-generated Code
• 3.2 Prompting for Bug Fixes and Test Coverage
• 3.3 Using AI-generated Unit Testing
• 3.4 Detecting Hallucinations and Unsafe Code
• 3.5 Hands-on Lab: AI-Assisted Debugging and Unit Testing
• 3.6 Activity Section

Module 4: Building a Simple Full-Stack App with Prompts

• 4.1 Planning the App: Frontend + Backend
• 4.2 Using IDEs and Code Generators to Scaffold Code
• 4.3 Connecting Components Using Natural Language
• 4.4 Deploying and Testing the MVP in Simulated Environment
• 4.5 Hands-on Lab: Building and Connecting the Frontend and Backend for Contact Form Submission
• 4.6 Hands-on Lab: Building a Standalone Desktop Calculator Application Using Tkinter
• 4.7 Hands-on Assignment 1: Task Management System – Full-Stack Development Using Prompts

Module 5: Code Ethics, Security, and AI Limits

• 5.1 AI Limitations and Biases
• 5.2 Prompt Injection and Mitigation Strategies
• 5.3 Data Privacy and Secure Coding
• 5.4 Responsible Use of AI in Production
• 5.5 Hands-on Lab: Build Awareness of AI Limitations and Responsible Practices

Module 6: Capstone Project – Prompt-Driven App

• 6.1 Apply All Learned Skills in a Real-World Project
• 6.2 Collaborate and Iterate Using AI Tools
• 6.3 Demonstrate End-to-End Development Using Prompts
• 6.4 Capstone Project Use Case: AI-Powered To-Do List Application
• 6.5 Capstone Project Use Case: AI-Powered Note-Taking Desktop App
• 6.6 Assignments

Tools you will explore

• Python
• TensorFlow
• PyTorch
• GitHub Copilot
• OpenAI Codex
• Hugging Face Hub
• LangChain
• FastAPI
• VS Code
• Jupyter Notebooks
• Pandas
• NumPy
• Scikit-learn
• Docker
• Streamlit
• API Integration Tools
• Prompt Engineering Frameworks
• Automation SDKs
• Version Control Systems (Git)

Exam: 50 questions, 70% passing, 90 minutes, online proctored examInstructor-led OR Self-paced course + Official exam + Digital badge