RAG Architecture | Offline & Online Pipeline | GenAI Series Ep 6

Welcome to Episode 6 of the “Generative AI & Agentic AI” Series 🚀 In the previous episode, we explored the core components of RAG systems such as chunking, embeddings, vector databases, retrievers, and augmentation layers. Now in this video, we are moving one step deeper and understanding the complete architecture and workflow of RAG systems 🔥 We will also clarify one of the biggest misconceptions in RAG: 👉 Offline indexing does NOT mean the data only comes from local files. In this video, we will explore: 👉 Difference between RAG Components, Workflow, and Architecture 👉 Offline Indexing Pipeline 👉 Online Retrieval Pipeline 👉 Generation Phase 👉 Maintenance & Data Refresh Pipeline 👉 Live Retrieval in Advanced RAG Systems Topics Covered: 📌 Data Ingestion & Indexing Layer 📌 Chunking, Embeddings & Vector Storage Flow 📌 Query Encoding & Semantic Search 📌 Top-k Retrieval 📌 Prompt Augmentation 📌 How LLM generates grounded responses 📌 Offline vs Online Pipelines 📌 RAG Maintenance & Re-embedding 📌 Challenges of RAG Systems 📌 Latency Issues in RAG 📌 Managing Unstructured Data By the end of this video, you will clearly understand how a complete RAG architecture works internally in production-grade AI systems. This video is beginner-friendly and highly useful for developers learning: ✅ Generative AI ✅ LLM Engineering ✅ AI System Design ✅ LangChain ✅ Enterprise AI Applications ✅ AI Chatbots & Document Q&A Systems