If you want to learn:
How does retrieval augmented generation (RAG) actually work behind the scenes? What are vector databases and why are they essential for RAG systems? How do embeddings and semantic search enable LLMs to access real-time information? What's the difference between traditional RAG and agentic RAG architecture? How can you implement RAG to give your AI applications access to company knowledge bases?
Then this lecture is for you!
This lecture demystifies how RAG works by walking you through the complete RAG pipeline step-by-step. You'll discover how retrieval-augmented generation transforms user queries into vector embeddings using an embedding model, then performs semantic search within a vector database to retrieve relevant information. The lecture explains the RAG architecture in detail: how questions are vectorized, how vector databases store and query data based on vector similarity, and how retrieved context gets inserted into the LLM prompt to generate accurate responses. You'll learn why modern databases like PostgreSQL and MongoDB now support vector search, understand that the embedding model operates independently from the language model, and see how RAG provides real-time access to external knowledge without retraining. The lecture covers practical use cases for RAG including HR systems, customer support, and knowledge retrieval applications where LLMs need expertise about company products, policies, and data. You'll also get introduced to advanced RAG techniques like graphRAG, hierarchical RAG, and re-ranking, plus learn why RAG evaluation and measurement are critical for optimizing RAG systems. Finally, discover what makes agentic RAG different from traditional RAG approaches and how this RAG technology is transforming generative AI applications.