Udemy - Enterprise RAG (Retrieval-Augmented Generation) Frameworks

Enterprise RAG (Retrieval-Augmented Generation) Frameworks
https://WebToolTip.com
Published 7/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 1h 42m | Size: 1.46 GB
Build scalable, governed RAG systems using hybrid retrieval, vector databases, LangChain, LlamaIndex, and GraphRAG.
What you'll learn

Design end-to-end RAG pipelines separating offline data indexing from online query execution.

Process heterogeneous enterprise documents using layout-aware parsing and robust extraction methods.

Implement semantic, fixed-size, and parent-document chunking strategies for optimal retrieval.

Deploy hybrid retrieval systems combining dense vector search and BM25 sparse lexical retrieval.

Apply cross-encoder reranking to elevate precision and filter irrelevant context before generation.

Utilize advanced patterns including HyDE, multi-query expansion, and GraphRAG for complex queries.

Enforce document-level access control, metadata filtering, and PII redaction inside the vector store.

Evaluate RAG systems using context precision, recall, and the RAGAS framework with LLM judges.
Requirements

Basic understanding of large language models (LLMs) and natural language processing concepts.

Familiarity with Python programming and API integrations.

Fundamental knowledge of database operations; prior vector database experience is beneficial but not strictly required.