AI+ Vibe Coder™
Supercharge coding with AI+ Vibe Coder™ for smarter, faster creation
Certificate Code:
AP 111
About This Course
- 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
Certificate Overview
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 4 hours of content
Prerequisites
Basic Computer Skills, Understanding of algebra and basic statistics, Logical Thinking, Programming Curiosity, English Proficiency
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Course Modules
1
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
2
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
3
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
4
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
5
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
6
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
AI Tools You'll Learn
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
