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Module 1 - Introduction to RAG

Overview​

This module introduces you to Retrieval-Augmented Generation (RAG), one of the most important paradigms in modern AI applications. You'll learn what RAG is, why it exists, and how it solves critical limitations of Large Language Models.

Learning Objectives​

By the end of this module, you will be able to:

  • Define RAG and explain its core components
  • Understand why RAG is necessary (LLM limitations)
  • Describe the three phases of RAG: indexing, retrieval, and generation
  • Visualize the end-to-end RAG pipeline
  • Identify use cases where RAG excels

Module Content​

1. What is RAG?​

  • Definition and etymology
  • The "retrieval + generation" concept
  • How RAG differs from fine-tuning
  • Brief history of RAG

2. Why RAG? Problems with Pure LLMs​

  • Hallucinations and factual errors
  • Knowledge cutoff dates
  • Lack of access to private data
  • Token limits and context windows

3. RAG Architecture Overview​

  • Indexing phase
  • Retrieval phase
  • Generation phase
  • Complete pipeline visualization

Estimated Duration​

2-3 hours (reading + exercises + quiz)

Prerequisites​

  • Basic understanding of LLMs (GPT, Claude, etc.)
  • Familiarity with APIs
  • No coding required for this module

Module Structure​

Module 1
├── What is RAG? (Definition and concepts)
├── Why RAG? (Problems with LLMs)
├── RAG Architecture (The three phases)
└── Quiz (30 questions)
  1. What is RAG? - Complete introduction
  2. Why RAG? - LLM limitations
  3. RAG Architecture - The pipeline
  4. Quiz - Module 1 - 30 questions

Key Takeaways Preview​

After completing this module, remember:

  1. RAG = Retrieval + Generation - Combining search with LLMs
  2. RAG solves hallucinations - By grounding responses in real data
  3. Three phases - Index → Retrieve → Generate
  4. RAG vs Fine-tuning - RAG for dynamic data, fine-tuning for behavior

Let's begin your RAG journey!