A visual walkthrough of the RAG (Retrieval-Augmented Generation) pipeline, covering each stage from query ingestion and embedding generation, through vector database similarity search and top-K chunk retrieval, to context injection into an LLM prompt and grounded response generation. Key concepts include cosine similarity, tunable K parameter, and how citations reduce hallucinations.