Class 18: Retrieval Augmented Generation (RAG)

Summary: Master RAG techniques that enhance LLM accuracy by connecting models to external knowledge sources.

Learning Objectives:

• Understand RAG architecture and workflow
• Implement vector databases for semantic search
• Build RAG-powered applications

Key Topics:

• RAG fundamentals and architecture patterns
• Vector embeddings and semantic similarity
• Vector database technologies: Pinecone, Weaviate, Chroma
• Retrieval strategies and ranking algorithms

Activities:

• Build a knowledge base chatbot using RAG
• Document Q&A system implementation
• RAG performance optimization exercises