GenerativeAICourse: A Comprehensive Hands-On Generative AI Engineering Course

GenerativeAICourse: A Comprehensive Hands-On Generative AI Engineering Course

Summary

This repository offers a comprehensive, hands-on Generative AI course, starting from fundamental AI concepts to building production-grade applications. It focuses on AI engineering, covering topics like LLMs, RAG, AI agents, and prompt engineering with practical tutorials. The course aims to equip learners with the skills needed to build real-world AI solutions.

Repository Info

Updated on December 4, 2025
View on GitHub

Tags

Generative AIAI EngineeringLLMsRAGAI AgentsJupyter NotebookMachine LearningPython

Click on any tag to explore related repositories

Introduction

The GenerativeAICourse repository offers an exceptionally comprehensive and hands-on course designed to teach Generative AI from foundational principles to building production-grade applications. It demystifies complex concepts, starting with the basics of AI and the evolution of Large Language Models (LLMs), then quickly transitioning into practical, real-world application development. This course is specifically tailored for those looking to master AI engineering, a rapidly expanding field focused on building robust applications on top of powerful, readily available AI models. It distinguishes itself by providing clear, jargon-free explanations and practical relevance, addressing the common shortcomings of overly academic or confusing AI courses.

Installation

Getting started with the GenerativeAICourse is straightforward, with clear instructions provided to set up your development environment. The process typically involves these key steps:

  • Install Essential Tools: Begin by installing Visual Studio Code and Git, which are fundamental for development and version control.
  • Clone the Repository: Obtain the course materials by cloning the GenerativeAICourse repository from GitHub to your local machine.
  • Set Up Python Environment: Install the necessary Python and Jupyter extensions in VS Code, then create and activate a Python virtual environment to manage dependencies.
  • Configure API Key: Create a .env file within the content folder and add your OpenAI API key, ensuring secure access to AI models.
  • Install Dependencies: Use pip to install required Python packages such as openai, python-dotenv, and jupyter.
  • Verify Setup: Test your environment by running a Jupyter notebook cell to confirm the API key is correctly configured.

Detailed, step-by-step instructions for each of these stages, including troubleshooting tips, are available directly within the repository's README.

Examples

Throughout the GenerativeAICourse, you will engage in numerous hands-on tutorials and projects, gaining practical experience in various aspects of AI engineering. Key areas of focus and examples of what you will build include:

  • Deploying Local LLMs: Learn how to run Large Language Models locally.
  • Building End-to-End AI Chatbots: Develop complete chatbot applications and master context management.
  • Prompt Engineering: Acquire skills in crafting effective prompts for optimal AI responses.
  • Defensive Prompting: Understand techniques to prevent common AI exploits and ensure robust applications.
  • Retrieval-Augmented Generation (RAG): Implement RAG systems to enhance AI models with external knowledge.
  • AI Agents and Advanced Use Cases: Explore the creation of intelligent AI agents for complex tasks.
  • Model Context Protocol (MCP): Delve into advanced protocols for managing model context.
  • LLMOps: Learn best practices for deploying and managing LLM-based applications in production.
  • Working with Good Data for AI: Understand the characteristics of high-quality data essential for effective AI solutions.

Why Use

The GenerativeAICourse stands out as an invaluable resource for anyone serious about building practical AI applications. Its unique advantages include:

  • Comprehensive Coverage: It offers a deep dive into Generative AI, from theoretical foundations to advanced application development.
  • Hands-On Learning: The course emphasizes practical tutorials, ensuring you gain tangible skills and experience.
  • Focus on AI Engineering: It directly addresses the growing demand for AI engineers, providing the specific knowledge and tools needed for this discipline.
  • Jargon-Free and Practical: Unlike many academic courses, it explains concepts clearly and focuses on real-world relevance, making complex topics accessible.
  • Scalable Solutions: Learn what it truly takes to build production-grade AI applications that can scale.
  • Expert-Led Insights: The course incorporates insights from extensive client work, reflecting what is genuinely required to build effective AI solutions.

Links