Introduction to RAG Systems
Discover how Retrieval-Augmented Generation transforms AI applications by giving language models access to external knowledge. Learn when to use RAG, how it works, and why it's revolutionizing how we build AI systems that need current, private, or specialized information.
What You'll Learn
- • Why RAG solves the "frozen knowledge" problem of LLMs
- • The complete RAG pipeline: retrieve, augment, generate
- • When to choose RAG vs. fine-tuning vs. prompt engineering
- • Real-world RAG applications across industries
- • Technical components and business ROI considerations
Prerequisites
- • Basic understanding of AI/LLMs (ChatGPT user level)
- • Interest in practical AI applications
- • Business or technical background helpful but not required
- • No coding required - focuses on concepts and strategy
Course Outline & Time Estimates
Introduction
Welcome to the world of Retrieval-Augmented Generation (RAG...
Prerequisites and Preparation
Chapter 0: Essential RAG Terminology Guide
*Your reference for all technical terms used throughout thi...
Chapter 1: The RAG Revolution: Why It Changes Everything
Chapter 2: How RAG Works: The Complete Mental Model
Chapter 3: The Four Pillars of RAG Systems
Understanding RAG requires grasping its four fundamental co...
Chapter 4: RAG vs. Alternatives: When to Choose What
Understanding when RAG is the right solution requires compa...
Chapter 5: Real-World Use Cases Across Industries
RAG systems are transforming how organizations handle infor...
Chapter 6: Understanding the Technical Pipeline
While you don't need to implement RAG from scratch, underst...
Chapter 7: Business Impact and ROI
Understanding the business value of RAG helps justify inves...
Chapter 8: Common Challenges and Solutions
Every RAG implementation faces predictable challenges. Unde...
Chapter 9: Future of RAG Technology
Understanding where RAG is heading helps you make informed ...
Chapter 10: Getting Started: Your RAG Journey
Based on your understanding of RAG concepts, here's how to ...
Chapter 11: Conclusion: RAG as a Transformative Technology
Retrieval-Augmented Generation represents a fundamental shi...
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