Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
RAG Foundation Course: End-to-End Generative AI Basics
New

RAG Foundation Course: End-to-End Generative AI Basics

Learn RAG fundamentals, retrieval pipelines, embeddings, and real-world AI systems in a simple end-to-end way
New
Created byKrishna Tadi
Last updated 6/2026
English

What you'll learn

  • Understand the fundamentals of Retrieval-Augmented Generation (RAG), its architecture, components, and real-world applications.
  • Learn and compare different RAG approaches, patterns, and architectures used in modern AI systems.
  • Master document chunking strategies and retrieval techniques to improve the accuracy and relevance of RAG systems.
  • Apply prompt engineering techniques within RAG workflows to generate more reliable, contextual, and accurate AI responses.

Explore related topics

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

10 sections • 40 lectures • 1h 24m total length

  • What is Retrieval-Augmented Generation (RAG)?1:05
  • Understanding RAG Architecture: End-to-End Workflow1:12
  • Why Do We Need RAG in AI Applications?1:02
  • Core Components of a RAG System Explained1:08
  • RAG Fundamentals Practice Quiz

Requirements

  • No prior RAG experience is required; a basic understanding of AI, LLMs, or Python is helpful but not necessary, as all concepts are explained from the ground up.

Description

Welcome to the RAG Foundation Course, your complete beginner-friendly guide to understanding how modern Generative AI systems work.

In this course, you will learn Retrieval-Augmented Generation (RAG) from the ground up in a simple and structured way. RAG is the core technology behind many AI applications like ChatGPT-style assistants, document search tools, and intelligent knowledge systems.

We will start with the fundamentals of RAG and gradually move toward how real AI systems are designed. You will understand the complete RAG pipeline, including how documents are processed, how chunking works, how retrieval systems find relevant information, and how large language models generate accurate responses using that context.

You will also explore different types of RAG systems, retrieval strategies, prompt engineering techniques, and optimization methods that improve performance and accuracy. A dedicated section on evaluation will help you understand how RAG systems are tested using key metrics like precision, recall, and answer quality.

To make learning practical, the course also includes real-world use cases such as chatbots, customer support systems, document Q&A tools, and enterprise AI assistants.

By the end of this course, you will have a strong conceptual understanding of RAG systems and how they power modern Generative AI applications, giving you a solid foundation to move into advanced AI topics.

Who this course is for:

  • This course is designed for AI enthusiasts, students, developers, data professionals, and technology leaders who want to understand Retrieval-Augmented Generation (RAG). It is ideal for anyone looking to learn how modern AI applications use retrieval, chunking, prompt engineering, and different RAG architectures to improve accuracy and reduce hallucinations. Whether you are new to RAG or seeking a strong conceptual foundation before building production systems, this course will provide a clear and practical understanding of the key concepts and techniques.

Report abuse