Fundamentals of RAG(Retrieval Augmented Generation)

About this course

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 As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.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 intel

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