Intermediate
Retrieval-Augmented Generation (RAG)
Build RAG systems that ground LLM responses in external knowledge. Learn about document processing, embeddings, vector databases, and retrieval optimization.
Introduction
Build RAG systems that ground LLM responses in external knowledge. Learn about document processing, embeddings, vector databases, and retrieval optimization.
4 Lessons
30h Est. Time
4 Objectives
1 Assessment
By completing this module you will be able to:
✓ Design and implement retrieval pipelines
✓ Work with vector databases and semantic search
✓ Optimize retrieval relevance and efficiency
✓ Implement hybrid retrieval strategies
Lessons
Work through each lesson in order. Each one builds on the concepts from the previous lesson.
1
RAG Architecture and Vector Databases
2
Building an End-to-End RAG Pipeline
3
Advanced Retrieval Strategies
4
RAG Evaluation and Optimization
Recommended Reading
Supplement your learning with these selected chapters from the course library.
Building Data-Driven Applications with LlamaIndex
Chapters 1-6
Unlocking Data with Generative AI and RAG
Chapters 1-7
RAG-Driven Generative AI
Chapters 1-5
Module Assessment
Retrieval-Augmented Generation (RAG)
Question 1 of 3