Скачать или смотреть ADVANCED RAG (Retrieval-Augmented Generation) Concepts & Architecture

Welcome to the most complete and beginner-friendly explanation of Advanced Retrieval-Augmented Generation (RAG)!
If you already understand the basics of RAG and now want to learn its architecture, workflows, retrieval methods, vector databases, chunking strategies, and enterprise-level patterns, this video will give you a clear, structured, and practical understanding.

In this video, you will learn:

🔹 What RAG actually does in real-world AI systems
🔹 How retrieval + generation work together
🔹 Dense, sparse & hybrid retrieval (BM25, embeddings, etc.)
🔹 Why vector search is essential for modern AI
🔹 Chunking strategies (fixed, semantic, recursive, overlap windows)
🔹 How embeddings represent meaning
🔹 The role of vector databases like Pinecone, FAISS, Azure AI Search
🔹 Azure end-to-end RAG workflow
🔹 Advanced RAG patterns (multi-step retrieval, agents, query expansion, GraphRAG)
🔹 Challenges & best practices for enterprise deployment

This video is designed using a clean, modern PPT presentation to make complex concepts simple and easy to understand.

⭐ What You Will Understand After Watching

✔ Complete RAG architecture end-to-end
✔ How AI retrieves the right information
✔ Why hallucinations happen & how RAG reduces them
✔ How companies use RAG in support bots, policy search, knowledge assistants
✔ Real enterprise workflows used in Azure, AWS, and other platforms
✔ The future of RAG and intelligent retrieval systems

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