Generative AI Genius

Generative AI Genius is a comprehensive 20-day program designed to help you understand, apply, and build with modern generative AI technologies. The course begins with the fundamentals of Generative AI, explaining how it differs from traditional AI and exploring real-world applications in text, image, audio, and video generation.

You’ll learn core concepts such as Large Language Models (LLMs), embeddings, tokens, and Retrieval-Augmented Generation (RAG), followed by practical prompt engineering techniques like zero-shot, few-shot, and chain-of-thought prompting. Through hands-on practice with tools like ChatGPT, Claude, Gemini, and text-to-image platforms, you’ll create content, build mini projects, and explore simple AI-powered applications.

What Will You Learn ?

Module 1

Introduction to Generative AI

• What is Generative AI and how it differs from traditional AI/ML
• Real‑world examples: text, images, audio, video generation
• Key technologies behind generative models (LLMs, GANs, diffusion models)
• How Generative AI is reshaping industries and workflows

Module 2

Foundational Concepts

• Overview of machine learning vs deep learning
• Large Language Models (LLMs) — architecture and capabilities
• Tokens, embeddings, and model behavior fundamentals
• Retrieval‑Augmented Generation (RAG) and why it’s important

Module 3

Prompt Engineering Essentials

• What prompts are and how they guide model responses
• Prompt techniques: zero‑shot, few‑shot, chain‑of‑thought
• Structuring effective prompts for different outcomes
• Controlling creativity, accuracy, and verbosity

Module 4

Practical Tools & Hands‑On

• Text Generation
• Image & Multimedia
• Project Work

Module 5

Advanced Techniques

• Fine‑tuning models / customizing to domain data
• Vector databases & similarity search
• One‑shot vs few‑shot learning strategies
• Basic deployment essentials: APIs & wrappers

Module 6

Ethics & Responsible AI

• Bias, fairness, and safety in generative outputs
• Understanding hallucinations and limitations
• Best practices for transparent use and validation