Beginner Free 7 hours

RAG Fundamentals: From Documents to Grounded Answers

Ingestion, chunking, embeddings, retrieval, and cited generation.

A rigorous beginner course on the complete retrieval-augmented generation pipeline from document parsing and chunking through embeddings, retrieval, prompting, and grounded answers. The curriculum is grounded in the curated technical references and preserves their technical depth, code, diagrams, decision rules, and failure analysis. Every lesson adds objectives, an applied lab, review questions, and concrete completion evidence. The course closes with a substantial capstone: Build a cited RAG assistant over a real document collection with repeatable ingestion and a baseline retrieval evaluation.

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What you will learn

Leave with a working mental model.

  1. 01 Explain the ingestion and query pipelines and their distinct failure modes
  2. 02 Choose loaders, chunking strategies, embedding models, and vector stores
  3. 03 Implement retrieval and context-grounded generation
  4. 04 Create a small question set and inspect retrieval evidence

Before you begin

Prerequisites

  • Python fundamentals
  • Basic LLM prompting
  • Familiarity with APIs and structured data

Curriculum

Course contents

7 modules · 14 lessons

01 Module 1: RAG Mental Model 4 lessons
  1. 01 What Is RAG Preview
  2. 02 Why RAG Exists Preview
  3. 03 RAG Core Concepts
  4. 04 RAG Architecture Overview
02 Module 2: Document Ingestion 2 lessons
  1. 01 RAG Document Loading and Parsing
  2. 02 RAG Chunking Strategies
03 Module 3: Embeddings and Vector Stores 2 lessons
  1. 01 RAG Embeddings and Models
  2. 02 RAG Vector Stores
04 Module 4: Retrieval 1 lesson
  1. 01 RAG Retrieval Strategies
05 Module 5: Grounded Generation 1 lesson
  1. 01 RAG Generation and Prompting
06 Module 6: Use Cases and Failure Modes 2 lessons
  1. 01 RAG Real-World Use Cases
  2. 02 RAG Common Pitfalls
07 Module 7: Capstone Studio 2 lessons
  1. 01 Capstone Brief and Architecture
  2. 02 Validation, Review, and Definition of Done