Category Deep Learning Chapters 7 Difficulty advanced Estimated Time 180 min

RAG & Retrieval Systems

Master Retrieval-Augmented Generation (RAG) systems from vector databases to production deployment. 7 comprehensive chapters covering embeddings, retrieval strategies, RAG architecture, and building production systems.

Course Overview

What You Will Build Toward

  • Navigate the RAG & Retrieval Systems learning path across 7 chapters.
  • Choose the right chapter based on your current goal and prerequisites.
  • Move from overview material into the canonical chapter experience.

Chapter Path

Start With Any Chapter

Chapter 1 Introduction to RAG Open chapter Chapter 2 Text Embeddings & Vector Representations Open chapter Chapter 3 Document Processing & Chunking Open chapter Chapter 4 Vector Databases Open chapter Chapter 5 Retrieval Strategies Open chapter Chapter 6 Advanced RAG Techniques Open chapter Chapter 7 Production RAG Systems Open chapter

Before You Start

Recommended Background

  • Working knowledge of the course category.
  • Willingness to work through examples and short checks.

Start Chapter 1

RAG & Retrieval Systems

Master Retrieval-Augmented Generation (RAG) systems from vector databases to production deployment. 7 comprehensive chapters covering embeddings, retrieval strategies, RAG architecture, vector databases, and building production RAG systems with detailed explanations, formulas, and code examples.

Chapter 1: Introduction to RAG

Why RAG? The Problem with LLMs

  • Limitations of LLMs (hallucination, outdated info)
  • What is RAG? Architecture overview
  • Retrieval + Generation pipeline
  • RAG vs fine-tuning comparison
  • When to use RAG
RAG Foundation Architecture

Chapter 2: Text Embeddings & Vector Representations

Converting Text to Vectors

  • Embedding models (OpenAI, Sentence-BERT)
  • Dense vs sparse embeddings
  • Embedding dimensions and quality
  • Semantic similarity and cosine distance
  • Embedding generation and storage
Embeddings Vectors Semantic

Chapter 3: Document Processing & Chunking

Preparing Documents for Retrieval

  • Document loading and parsing
  • Text chunking strategies
  • Fixed-size vs semantic chunking
  • Overlap and context preservation
  • Metadata extraction and storage
Chunking Processing Documents

Chapter 4: Vector Databases

Storing and Searching Embeddings

  • What are vector databases?
  • Pinecone, Weaviate, Chroma, FAISS
  • Indexing strategies (HNSW, IVF)
  • Similarity search algorithms
  • Vector database setup and operations
Vector DB Search Indexing

Chapter 5: Retrieval Strategies

Finding the Right Context

  • Dense retrieval (semantic search)
  • Sparse retrieval (BM25, keyword search)
  • Hybrid retrieval (combining dense + sparse)
  • Re-ranking strategies
  • Retrieval evaluation metrics
Retrieval Search Hybrid

Chapter 6: Building RAG Systems

End-to-End Implementation

  • RAG pipeline architecture
  • Context assembly and prompt construction
  • LLM integration (OpenAI, Anthropic, local)
  • Error handling and fallbacks
  • Complete RAG implementation example
Implementation Pipeline Production

Chapter 7: Advanced RAG & Optimization

Production-Ready RAG Systems

  • Query expansion and rewriting
  • Multi-step retrieval
  • RAG evaluation and metrics
  • Performance optimization
  • Monitoring and debugging RAG systems
Advanced Optimization Production