Fundamentals of RAG(Retrieval Augmented Generation)

Master the Foundations of Retrieval-Augmented Generation (RAG) to Build Smarter, Context-Aware AI Applications

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

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 As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.That’s where Retrieval-Augmented Generation (RAG) comes in.This course, Fundamentals of RAG, is designed to help you understand and apply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 gapThe 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 frameworksHands-on projects to help reinforce learning through practical applicationHands-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 skillsDevelopers building intelligent search and assistant solutionsProduct managers and innovators exploring real-world applications of GenAIAnyone curious about how LLMs can go beyond training data to create dynamic, responsive systemsBy 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.