[h1]Free download скачать Udemy - Fundamentals of RAG(Retrieval Augmented Generation)[/h1]
Published: 4/2025
Created by: Pramod Kumar Singh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 12 Lectures ( 1h 27m ) | Size: 1 GB
Master the Foundations of Retrieval-Augmented Generation (RAG) to Build Smarter, Context-Aware AI Applications
[h2]What you'll learn[/h2]
"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
[h2]Requirements[/h2]
Basic understanding of Python, Generative AI and Language Model
[h2]Description[/h2]
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 As the world changes and new knowledge is created every day, relying solely on pre-trained models can lead to outdated or incomplete answers.That's where Retrieval-Augmented Generation (RAG) comes in.This course, Fundamentals of RAG, is designed to help you understand and apply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 [h2]Description[/h2]s 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!
[h2]Who this course is for[/h2]
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.
Homepage:
https://www.udemy.com/course/fundamentals-of-ragretrieval-augmented-generation/
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