What Is RAG? How AI Finds the Right Answer from Your Own Documents

Retrieval-Augmented Generation (RAG) is a technique that allows AI models to answer questions using information retrieved from specific document sources, rather than relying solely on their pre-trained knowledge. The process works in two broad stages: preparing a knowledge base from documents such as PDFs, manuals, or policy files, and then retrieving relevant sections to help the AI generate accurate answers. Documents are broken into smaller chunks so only the most relevant pieces are passed to the model, improving both speed and precision. Embeddings — numerical representations of text — are used to find information based on meaning rather than exact keyword matches. RAG is particularly useful for enterprise applications where an AI needs to reference internal, organisation-specific data that a general model would not otherwise know.