In this post, you’ll understand the concept of RAG and learn how to implement it in an ASP.NET Core application, exploring a practical, real-world scenario.

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RAG (Retrieval-Augmented Generation) enhances language models by connecting them to internal knowledge sources for more accurate, context-aware responses without retraining. This tutorial demonstrates building a RAG system in ASP.NET Core that answers product return policy questions by converting policy text into embeddings stored in SQLite, retrieving relevant chunks via semantic search, and generating responses through OpenAI's API. The implementation covers the complete RAG workflow: user input, information retrieval using vector embeddings, context assembly, augmented generation, and response delivery. The article includes code for embedding generation, cosine similarity calculations, database operations, and integration with OpenAI's chat and embedding models.

Building a RAG in ASP.NET Core

Running the Application and Testing the RAG