Introduction to Generative AI
Course Curriculum
7 modules designed to master the subject.
Module 1: The Landscape of AI
Define AI, understand the hierarchy from ML to GenAI, and contrast discriminative vs. generative systems.
Module 1 Lesson 1: What Is Artificial Intelligence?
Defining AI in simple terms and exploring everyday examples of AI in our world.
Module 1 Lesson 2: The AI Hierarchy
Understanding how Generative AI sits within the broader fields of Machine Learning and Deep Learning.
Module 1 Lesson 3: Discriminative vs. Generative AI
Predicting labels vs. creating new data. Understanding the fundamental shift in how AI assists us.
Module 1 Lesson 4: A Brief History of Generative AI
From rule-based systems to GANs and the massive Transformer breakthrough.
Module 1 Wrap-up: Mapping the AI World
Reviewing the AI landscape and testing your ability to distinguish between different AI types.
Module 2: Large Language Models (LLMs) and Transformers
Deep dive into how LLMs work, tokenization, embeddings, and the revolutionary Transformer architecture.
Module 2 Lesson 1: What Are Large Language Models (LLMs)?
Understanding the scale, training, and significance of models like GPT-4 and Claude.
Module 2 Lesson 2: How LLMs Work (Conceptual)
From Tokens to Embeddings. Understanding the mechanics of how a computer 'reads' meaning.
Module 2 Lesson 3: The Transformer Architecture
Attention Mechanisms and Context. Understanding the 'Secret Sauce' that allows AI to reason across long documents.
Module 2 Lesson 4: Hallucinations and Bias
Common failure modes. Why AI makes things up and how to detect biased or incorrect outputs.
Module 2 Lesson 5: Popular LLMs Overview
Choosing your model. Comparing the strengths and tradeoffs of GPT-4, Claude, Gemini, and Llama.
Module 2 Wrap-up: Understanding the Engine
Reviewing the mechanics of LLMs and conducting a comparative model experiment.
Module 3: The Art of Prompt Engineering
Master techniques like few-shot, chain-of-thought, and system prompting to control AI behavior.
Module 3 Lesson 1: Why Prompting Matters
The Prompt as an Interface. Understanding why the quality of your input directly determines the utility of the AI.
Module 3 Lesson 2: Foundational Prompting Techniques
Zero-shot, Few-shot, and Chain-of-Thought. The three pillars of professional AI interaction.
Module 3 Lesson 3: System Prompts
Defining Persona and Role. How to create an 'Invisible' layer of instructions that controls the AI's personality and safety.
Module 3 Lesson 4: Structuring Outputs
From Text to Table. How to force the AI to return data in specific formats like JSON, Markdown, or CSV.
Module 3 Lesson 5: Iterative Prompt Design
The Refinement Loop. How to treat prompting as an experiment rather than a one-off command.
Module 3 Wrap-up: Becoming a Prompt Architect
Reviewing professional prompting techniques and completing a structured output project.
Module 4: Beyond Text (Multimodal AI)
Explore image generation (diffusion), audio, video, and AI-assisted coding.
Module 4 Lesson 1: Image Generation and Diffusion
How AI draws. Understanding Diffusion models and the tools used to create stunning visual content from text.
Module 4 Lesson 2: Audio and Video Generation
The Sound of AI. Exploring text-to-speech, music generation, and the emerging frontier of AI-generated video.
Module 4 Lesson 3: Code Generation
AI as a Coding Assistant. How to use LLMs to write, debug, and explain programming languages.
Module 4 Wrap-up: The Multimodal Creator
Reviewing non-text GenAI and practicing image generation and code refactoring.
Module 5: Building AI Applications (The Tech Stack)
Understand RAG, Vector Databases, AI Agents, and local LLMs using Ollama.
Module 5 Lesson 1: Retrieval-Augmented Generation (RAG)
Connecting AI to Reality. How to ground AI responses in your own private data to prevent hallucinations.
Module 5 Lesson 2: Vector Databases
The Memory of AI. Understanding how we store and search 'Meaning' using embeddings and specialized databases.
Module 5 Lesson 3: Agentic AI and Orchestration
From Chatbot to Coworker. Understanding AI agents that can use tools and make autonomous decisions.
Module 5 Lesson 4: Local LLMs
AI on your desktop. Learn why and how to run powerful models locally for total privacy and zero cost.
Module 5 Wrap-up: Building with Intelligence
Reviewing the AI tech stack and building a simple RAG pipeline and local AI setup.
Module 6: Ethics, Safety, and the Future
Discuss responsible AI, security risks like prompt injection, and the future of human-AI collaboration.
Module 6 Lesson 1: Responsible AI
The Weight of Creation. Discussing deepfakes, copyright, and the environmental impact of large-scale AI.
Module 6 Lesson 2: Security and Privacy
Protecting the Prompt. Understanding prompt injection attacks and data leakage risks in AI systems.
Module 6 Lesson 3: The Future of Generative AI
Where we are heading. Discussing Small Language Models (SLMs), autonomous agents, and the new skills required for the AI era.
Module 6 Wrap-up: Designing the Future Responsibly
Finalizing the course with a discussion on ethics and an outlook on the AI co-pilot era.
Capstone Projects: Expert Chatbot & RAG
Build a constrained specialist chatbot and a 'Talk to Your PDF' RAG pipeline.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
Found this course useful? Support the creator to help keep it free for everyone.
Support the Creator