What Is RAG?

RAG stands for Retrieval-Augmented Generation. It is a technique that improves the accuracy and relevance of LLM responses by combining two components: a retrieval system (which searches for relevant information from a specific dataset) and a generation model (which uses that retrieved information to compose a response). The result is an AI that can answer questions grounded in your specific data, rather than relying solely on what it learned during training.

The problem RAG solves is one of the fundamental limitations of LLMs: they only know what they were trained on, and their training has a cutoff date. If you ask an LLM about your product documentation, your internal knowledge base, or recent events, it either does not know or makes something up. RAG fixes this by dynamically fetching relevant information at query time and providing it to the model as context.

A RAG system works in three steps: first, your source documents are processed and stored as vector embeddings in a vector database. When a user asks a question, the query is converted to a vector and compared against the stored embeddings to find the most relevant passages. Those passages are then included in the prompt sent to the LLM, which generates a response grounded in that retrieved context.

RAG is the standard architecture for building AI features that need to work with specific, up-to-date, or proprietary information, chatbots that answer questions about your product, search systems that surface relevant content, or assistants that reference your internal documentation. It is significantly more reliable than asking an LLM to answer from memory alone.