Generative AI Essentials Course Syllabus

Overview: This course offers a focused, beginner-friendly introduction to generative AI, combining core technical concepts with practical prompting techniques and ethical considerations. Designed by MAANG engineers, it walks you through the evolution, architecture, and real-world applications of generative models. With approximately 6.5 hours of content, the course includes interactive explanations, case studies, and hands-on exercises to reinforce learning—all in a text-based, accessible format perfect for newcomers. Upon completion, you'll receive a certificate and gain foundational knowledge applicable across industries.

Module 1: Introduction to Generative AI

Estimated time: 1 hour

• Definitions and core concepts of generative AI
• Historical development and key milestones
• Differences between generative and traditional AI
• Neural model basics and foundational principles

Module 2: Training & Scaling Models

Estimated time: 1.5 hours

• Pretraining processes and data requirements
• Fine-tuning pipelines and transfer learning
• Foundation models and large-scale LLM architectures
• Deployment considerations for scalable AI systems

Module 3: Text, Image & Audio Generation

Estimated time: 2 hours

• LLM text generation methods and autoregressive modeling
• Vision transformer workflows and design principles
• Masked image modeling techniques
• Audio generation strategies and multimodal integration

Module 4: Prompting & AI Communication

Estimated time: 1 hour

• Prompt engineering fundamentals
• Context-responsive prompting techniques
• Guiding outputs across different AI modalities

Module 5: Ethics, Safety & Responsible Use

Estimated time: 1 hour

• Understanding AI bias and fairness issues
• Deepfake risks and misinformation threats
• Environmental impact and regulatory considerations
• Best practices for ethical AI deployment

Module 6: Final Project

Estimated time: 1 hour

• Apply prompting strategies to a real-world scenario
• Analyze ethical implications in a case study
• Submit a reflective summary on responsible AI use

Prerequisites

• Familiarity with basic AI and machine learning concepts
• Basic understanding of neural networks (helpful but not required)
• No coding experience required

What You'll Be Able to Do After

• Explain the core principles and history of generative AI
• Compare pretraining and fine-tuning approaches in model development
• Apply effective prompting techniques to guide AI outputs
• Analyze risks related to bias, deepfakes, and misinformation
• Practice responsible AI use in real-world contexts