RAG Architecture Explained (In-Depth) | Gen AI Course

Описание: Confused by the buzzwords?

This video explains RAG (Retrieval-Augmented Generation) in depth—starting from what an LLM is, the challenges of plain LLMs (hallucinations, stale knowledge, privacy), and how RAG solves them with a practical pipeline: Retrieval → Augmentation → Generation.

We’ll also cover private knowledge bases, vector databases, data ingestion, and real use cases you can build today.

What you’ll learn
LLM basics: what large language models do (and don’t).
Why LLMs struggle: hallucinations, missing sources, outdated context, privacy/compliance.
*RAG to the rescue: grounding answers in your data to reduce hallucination.
*RAG steps (end-to-end):
Retrieval – search relevant docs (vector/hybrid)
Augmentation – assemble context windows, prompts, citations
Generation – produce grounded, source-linked outputs
Private knowledge base: organizing your PDFs, wikis, tickets, DB rows with metadata.
Vector DB 101: embeddings, indexes (HNSW/IVF), filters, re-ranking, caching.
Data ingestion for RAG: chunking strategies, dedupe, versioning, scheduled updates.
RAG use cases: customer support, internal search, policy Q&A, analytics assistants, code/helpdesk.

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