Module 7: RAG and Semantic Search

1. Module Introduction: RAG and Semantic Search

Next →

Overview

This module kicks off Phase 3: RAG and Applications. Here you connect everything you've learned (vectors, similarity, search) to understand RAG (Retrieval-Augmented Generation): how LLMs use semantic search to generate accurate, up-to-date answers.

Estimated time: 2-2.5 hours

Module Objectives

  1. Understand RAG conceptually (the problem, the solution, the architecture)
  2. Connect semantic search with LLMs
  3. Analyze the complete RAG flow (retrieval → augmentation → generation)
  4. Compare RAG vs fine-tuning
  5. Identify RAG's limitations and trade-offs

Roadmap: 7 capsules

#CapsuleDuration
01Introduction15 min
02The problem RAG solves20 min
03RAG architecture25 min
04The RAG flow step by step25 min
05RAG vs fine-tuning25 min
06RAG limitations20 min
07Capstone exercise30 min

Next capsule: 02-the-problem-rag-solves.md — Hallucinations, outdated knowledge.

← Previous8. Capstone Exercise: Designing a Search SystemNext →2. The Problem RAG Solves