Retrieval-Augmented Generation in .NET: A Practical RAG Guide If you're building AI applications with .NET, there is a point where simply sending a prompt to an LLM stops being enough. Generating a summary is easy. Generating an answer from your company's documents, policies, product information, support knowledge, database records, or internal documentation is a different problem. The model does not automatically know your private data. And even when information is available somewhere in a model's training data, you generally do not want an application to depend on the model remembering it correctly or knowing the latest version of a business policy. This is where Retrieval-Augmented Generation (RAG) becomes useful. Instead of asking an LLM to answer entirely from its general knowledge, your application first retrieves relevant information from an external knowledge source and provides that information to the model as context. RAG = retrieve releva...