RAG Architecture Explained (In-Depth) | Gen AI Course скачать в хорошем качестве

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

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 Why LLMs struggle: hallucinations, missing sources, outdated context, privacy/compliance.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. If this helped: 👍 Like • 🔔 Subscribe • 💬 Comment your stack & use case—I'll suggest a retrieval plan. Want to make career in Generative AI? Watch the complete Roadmap to make career in Gen AI - https://connect.genaielite.com/