RAG

RAG is the architecture that bridges the gap between an LLM’s frozen training data and your dynamic, proprietary data. It is the standard for building production AI applications that are accurate, grounded, and up-to-date.

Module Contents

1. RAG Fundamentals

Understand the core problem RAG solves: Hallucinations and Knowledge Cutoffs. Learn the RAG Triad (Retriever, Augmenter, Generator) and interact with a live RAG simulator.

2. Vector Databases

Dive deep into the “Long-Term Memory” of AI. Learn how Embeddings work, visualize High-Dimensional Space, and understand Similarity Search using Cosine Similarity and HNSW.

3. Advanced RAG Architectures

Move beyond Naive RAG. Master production techniques like Recursive Chunking, Hybrid Search (Keyword + Vector), Re-ranking with Cross-Encoders, and Query Expansion.

Review: Module 03

Test your knowledge with interactive flashcards, review key takeaways, and grab the RAG cheat sheet for your next system design interview.

Module Chapters