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AI+ Vibe Coder™

  • 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

All prices are in NZD, ex GST (15%).

At a Glance: Course + Exam Overview

Category AI Development
AI Professional
Program Name: AI+ Vibe Coder™
Exam Format 50 questions, 70% passing, 90 minutes

🤝 You’re never on your own — Parasol Concierge Support provides integration guidance and expert VA assistance so you can focus on mastering skills while we handle the setup details.

WHAT You'll Learn

AI-Assisted Coding

Learn how to write, optimize, and debug code using intelligent AI tools and natural language interfaces.

Machine Learning Fundamentals

Understand core ML concepts to build smarter, data-driven applications that adapt and improve over time.

Generative Development Techniques

Explore how generative AI can automate code generation, testing, and creative problem-solving in real-world projects.

Ethical and Responsible Coding

Gain awareness of responsible AI practices, ensuring transparency, fairness, and safety in AI-powered software solutions.

CERTIFICATION Modules

  1. 1.1 What is Vibe Coding?
  2. 1.2 Evolution of AI in Software Development – Low Code vs No Code vs Vibe Coding
  3. 1.3 Overview of Common AI Coding Tools by Functionality
  4. 1.4 SDLC for a Vibe Coding Product
  5. 1.5 Hands-on Lab: Familiarizing Learners with Multiple AI Coding Tools
  6. 1.6 Case Studies

  1. 2.1 Anatomy of a Good Prompt
  2. 2.2 Prompt Types – Instructive, Descriptive, Iterative
  3. 2.3 Prompting Patterns – Zero-Shot, Few-Shot, Chain-of-Thought
  4. 2.4 Hands-on Lab: Practice Zero-Shot, Few-Shot, and Chain-of-Thought Prompting
  5. 2.5 Use-Case 1: Creating a Python Calculator
  6. 2.6 Use-Case 2: Optimizing AI-generated Code Using Different Prompt Types

  1. 3.1 Reviewing and Refining AI-generated Code
  2. 3.2 Prompting for Bug Fixes and Test Coverage
  3. 3.3 Using AI-generated Unit Testing
  4. 3.4 Detecting Hallucinations and Unsafe Code
  5. 3.5 Hands-on Lab: AI-Assisted Debugging and Unit Testing
  6. 3.6 Activity Section

  1. 4.1 Planning the App: Frontend + Backend
  2. 4.2 Using IDEs and Code Generators to Scaffold Code
  3. 4.3 Connecting Components Using Natural Language
  4. 4.4 Deploying and Testing the MVP in Simulated Environment
  5. 4.5 Hands-on Lab: Building and Connecting the Frontend and Backend for Contact Form Submission
  6. 4.6 Hands-on Lab: Building a Standalone Desktop Calculator Application Using Tkinter
  7. 4.7 Hands-on Assignment 1: Task Management System – Full-Stack Development Using Prompts

  1. 5.1 AI Limitations and Biases
  2. 5.2 Prompt Injection and Mitigation Strategies
  3. 5.3 Data Privacy and Secure Coding
  4. 5.4 Responsible Use of AI in Production
  5. 5.5 Hands-on Lab: Build Awareness of AI Limitations and Responsible Practices

  1. 6.1 Apply All Learned Skills in a Real-World Project
  2. 6.2 Collaborate and Iterate Using AI Tools
  3. 6.3 Demonstrate End-to-End Development Using Prompts
  4. 6.4 Capstone Project Use Case: AI-Powered To-Do List Application
  5. 6.5 Capstone Project Use Case: AI-Powered Note-Taking Desktop App
  6. 6.6 Assignments

Industry opportunities

AI Software Developer

    Design and build intelligent applications that leverage machine learning models to automate processes and enhance user experiences.

Data-Driven Application Developer

    Develop smart apps powered by data analytics and predictive modeling to solve real-world business challenges.

Automation Solutions Architect

    Create AI-based automation pipelines that streamline coding workflows, boost productivity, and reduce manual effort.

Chief AI Innovation Engineer (CAIIE)

    Lead next-generation AI software initiatives, driving digital transformation and intelligent product development across industries.

FREQUENTLY ASKED QUESTIONS

Yes, this certification provides hands-on experience through real coding challenges and AI-driven projects. You’ll be ready to build intelligent applications and automate workflows from day one.

This certification uniquely combines programming fundamentals with AI-powered development, focusing on real-world automation, generative coding, and intelligent software creation.

You’ll work on projects like AI-assisted app development, automated code generation, chatbot creation, and a capstone project building an AI-powered coding assistant or tool.

The course blends expert-led lessons, interactive coding labs, and practical projects with real-world applications to ensure hands-on mastery of AI-integrated programming.

It equips you with high-demand AI coding skills, real-world project experience, and the technical foundation needed for emerging roles in AI software and automation development.

PREREQUISITES

  • Basic Computer Skills: Comfortable with operating systems and files.
  • Mathematics Fundamentals: Understanding of algebra and basic statistics.
  • Logical Thinking: Ability to approach problems step by step.
  • Programming Curiosity: Interest in learning coding from scratch.
  • English Proficiency: Ability to follow technical instructions clearly.

EXAM DETAILS

Duration

90 minutes

Passing Score

70%

Format

50 multiple-choice/multiple-response questions

EXAM BLUEPRINT

Introduction to Vibe Coding & AI Tools 15%
Prompting for Code - Basics & Best Practices 15%
Debugging & Testing via AI 15%
Building a Simple Full-Stack App with Prompts 20%
Code Ethics, Security, and AI Limits 20%
Capstone Project - Prompt-Driven App 15%

Instructor-Led (Live Virtual/Classroom)

Request Virtual Training

TECHNOLOGIES USED

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