Understanding RAG and LLM Workflow in AI with .NET 8

An introductory guide to RAG (Retrieval-Augmented Generation) and LLM workflows aimed at .NET developers. Explains why LLMs have knowledge limitations, how RAG addresses them by retrieving relevant context before generating responses, and why retrieval quality directly impacts answer accuracy. Includes a simple .NET 8 in-memory retrieval example to simulate the search step of a RAG pipeline, with a workflow summary table and a preview of upcoming topics like Azure AI Search and vector embeddings.