01Module 04: RAG SystemsMaster Retrieval-Augmented Generation - the dominant pattern for grounding LLMs in external knowledge at production scale.02Why RAG and When Not ToUnderstand why LLMs hallucinate, what RAG actually solves, and the decision framework for choosing RAG vs fine-tuning vs prompt stuffing.03Document Chunking StrategiesMaster the art and science of splitting documents into chunks that maximize retrieval precision - the most underestimated decision in RAG system design.04Embedding Models Deep DiveMaster embedding model selection for retrieval - MTEB benchmarks, model families, Matryoshka embeddings, bi-encoders vs cross-encoders, and fine-tuning strategies.05Vector DatabasesCompare Pinecone, Qdrant, Weaviate, Milvus, Chroma, and pgvector - understand the engineering trade-offs and build a production vector store.06Retrieval Algorithms and ANNMaster the approximate nearest neighbor algorithms powering vector search - HNSW, IVF, IVF-PQ, ScaNN, and DiskANN with parameter tuning and recall-latency trade-offs.07RerankingMaster the two-stage retrieval-reranking architecture - cross-encoders, ColBERT, LLM-as-reranker, Reciprocal Rank Fusion, and production latency budgets.08Hybrid Search: Dense and SparseCombine BM25 sparse retrieval with dense vector search for best-of-both-worlds performance - understand SPLADE, fusion methods, and when hybrid beats pure dense.09RAG EvaluationBuild rigorous RAG evaluation with RAGAS, TruLens, LLM-as-judge, golden datasets, and production monitoring - measure faithfulness, relevance, and groundedness.10Advanced RAG PatternsGo beyond naive RAG - master query transformation, HyDE, multi-query retrieval, Self-RAG, Corrective RAG, and iterative retrieval patterns for complex questions.11Graph RAGMaster Microsoft's Graph RAG - build knowledge graphs from documents, use community detection for global queries, and understand when graph structure beats flat vector search.12Agentic RAGBuild agents that control their own retrieval - multi-step reasoning, router agents, ReAct loops, LangGraph stateful pipelines, and production patterns for agentic retrieval systems.
