What Is RAG? A Beginner's Guide to AI That Reads Your Own Documents

Retrieval-Augmented Generation (RAG) is a technique that allows AI models to answer questions using your own documents rather than relying solely on their training data. The process involves loading files, splitting them into small chunks, converting those chunks into numerical vectors, and storing them in a vector database organized by meaning. When a user asks a question, the system embeds the query, finds the most relevant document chunks, and passes them to a large language model to generate a grounded response. This approach reduces AI hallucinations by giving the model access to specific, up-to-date source material before it writes an answer. RAG is increasingly used by companies to build internal AI assistants that can reference proprietary documents like HR policies, product manuals, or technical guides.