RAG Explained: How AI Systems Access Private Data Using Vector Search

Retrieval-Augmented Generation (RAG) is an AI architecture that enables large language models to query private or proprietary data sources, such as internal company documents, that are not part of their original training. The process begins with pre-processing, where documents are split into smaller text segments called chunks, which are then passed through an embedding model that converts each chunk into a multi-dimensional vector point stored in a vector database. When a user submits a query, the system finds the most relevant chunks using distance or cosine similarity algorithms and retrieves them as context. This retrieved context is combined with the original query to form an augmented prompt, which is then sent to an LLM like ChatGPT or Gemini to generate a grounded response. Dynamic documents can be handled by either recreating or appending to the vector database depending on how the source data changes.