01RAG Engineering - Module OverviewBuild production-grade Retrieval-Augmented Generation systems - from document ingestion and chunking through hybrid search, query transformation, agentic RAG, and RAGAS evaluation.02Why RAG ExistsUnderstand why Retrieval-Augmented Generation was invented, what problems it solves that fine-tuning and prompt stuffing cannot, and how to architect a minimal RAG pipeline from scratch.03Document Ingestion and ChunkingMaster every chunking strategy from fixed-size to semantic and structure-aware splitting. Learn how to parse PDFs, DOCX, and HTML, enrich metadata, evaluate chunk quality, and build a production-grade ingestion pipeline.04Embedding Models in ProductionHow to choose, deploy, and manage embedding models at scale - including versioning, caching, batching, and migration strategies for production RAG systems.05Vector Search in PracticeHow approximate nearest neighbor search works, how to choose the right vector database, and how to build production-grade retrieval pipelines that stay fast at millions of documents.06Hybrid Search and RerankingHow to combine BM25 sparse retrieval with dense vector search using Reciprocal Rank Fusion, and how to apply cross-encoder reranking for precision that neither method achieves alone.07Query Transformation and HyDEMaster query transformation techniques - HyDE, multi-query retrieval, step-back prompting, query decomposition, and routing - to solve the vocabulary mismatch problem that breaks naive RAG systems in production.08Agentic RAGBuild RAG systems that reason, iterate, and self-correct - covering Self-RAG, FLARE, ReAct tool-augmented RAG, RAPTOR, and Corrective RAG with full production implementations using the Anthropic SDK.09RAG Evaluation and RAGASBuild a continuous RAG evaluation pipeline using the RAGAS framework - faithfulness, answer relevance, context precision, and context recall - with full production implementations using the Anthropic SDK and automated regression detection.
