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Fundamentals of RAG(Retrieval Augmented Generation)
Rating: 4.0 out of 5(6 ratings)
1,017 students

Fundamentals of RAG(Retrieval Augmented Generation)

Master the Foundations of Retrieval-Augmented Generation (RAG) to Build Smarter, Context-Aware AI Applications
Created byPramod Kumar Singh
Last updated 3/2025
English

What you'll learn

  • "Why": Why do we even need RAG, and what unique challenges does it address in the world of Generative AI?
  • "What": What exactly is Retrieval-Augmented Generation? I’ll break down its key components and functionality.
  • "How": How to implement RAG in real-world applications.
  • Hands-on: Implement Real World Use Cases using RAG

Course content

5 sections • 12 lectures • 1h 27m total length

  • Introduction1:22

    Introduction: Why RAG Matters in Generative AI


    Welcome, everyone!

    No matter what domain you're working in—healthcare, finance, HR, or e-commerce—if you're building a Generative AI application, Retrieval-Augmented Generation (RAG) will likely become the backbone of your system. Whether you're designing agentic workflows or traditional AI solutions, RAG provides a powerful way to bridge the gap between static model knowledge and dynamic, real-world data.

    In this session on Fundamentals of RAG, my goal is to equip you with a strong foundational understanding of:

    • Why RAG is essential in modern GenAI systems

    • What RAG actually is, including its core components and inner workings

    • How to implement RAG effectively in real-world applications

    We’ll structure this session around a simple but powerful framework:
    Why → What → How

    ✅ First, we’ll explore the “Why” — Why do we need RAG at all? What limitations in large language models does it solve?
    ✅ Then, we’ll unpack the “What” — What exactly is RAG? We’ll demystify its architecture and how retrieval and generation come together.
    ✅ Finally, we’ll get hands-on with the “How” — How can you build a working RAG pipeline using practical tools and frameworks?

    To bring these ideas to life, we’ll implement two hands-on projects:

    • LiveStockIQ: An app that fetches real-time stock data from third-party APIs based on user queries—demonstrating how RAG enhances financial data intelligence.

    • SmartRecruit: A recruitment assistant for HR teams that uses RAG to screen resumes and match candidates to roles—showcasing how RAG can streamline hiring workflows.

    By the end of this session, you won’t just know what RAG is—you’ll be ready to build with it. Let’s dive in!

Requirements

  • Basic understanding of Python, Generative AI and Language Model

Description

Unlock the Power of Generative AI with Retrieval-Augmented Generation (RAG)!

In today’s rapidly evolving AI landscape, traditional language models—no matter how large—face a common limitation: they are bound by the static nature of their training data. As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.

That’s where Retrieval-Augmented Generation (RAG) comes in.

This course, Fundamentals of RAG, is designed to help you understand and apply this cutting-edge architecture that combines the dynamic strengths of information retrieval with the generative power of large language models (LLMs). Whether you're building AI agents, chatbots, intelligent assistants, or search-enhanced applications, RAG will become a cornerstone of your solution.

We’ll start by demystifying RAG’s architecture and real-world importance:

What You’ll Learn:

Why traditional LLMs fall short when it comes to dynamic, real-time, or domain-specific information—and how RAG fills the gap
The core components of RAG: Retrieval (searching from external knowledge bases) and Generation (using LLMs to produce rich responses)
How to design, build, and deploy RAG systems from scratch using popular tools and frameworks
Hands-on projects to help reinforce learning through practical application

Hands-On Use Cases:

We’ll guide you through two real-world RAG implementations that you can apply and extend in your own projects:

  • LiveStockIQ – A stock market assistant that integrates with real-time financial APIs to provide current stock data, company info, and market trends. You’ll see how retrieval connects to APIs and how LLMs generate insights on top of it.

  • SmartRecruit – An AI-powered recruitment assistant for HR teams that intelligently analyzes resumes and matches them to job descriptions using contextual document retrieval and summarization.

Who Is This Course For?

This course is perfect for:

  • AI/ML engineers and data scientists looking to level up their GenAI skills

  • Developers building intelligent search and assistant solutions

  • Product managers and innovators exploring real-world applications of GenAI

  • Anyone curious about how LLMs can go beyond training data to create dynamic, responsive systems

By the end of this course, you won’t just understand what RAG is—you’ll be able to implement it, customize it, and integrate it into your own AI solutions.

Get ready to take your Generative AI projects to the next level with the Fundamentals of RAG!

Who this course is for:

  • Whatever domain you are working in, if you are building a Generative AI application—whether it's agentic or non-agentic—RAG, or Retrieval-Augmented Generation, will undoubtedly form the heart of your system. In this cpurse , I aim to provide you with a solid understanding of WHY RAG is so crucial, WHAT it actually is, and HOW to effectively implement it.

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