RAG (Retrieval-Augmented Generation)

Detailed definition

RAG (Retrieval-Augmented Generation) is a technique that supports a large language model's (LLM) answer with relevant documents retrieved from an enterprise knowledge base at query time. Instead of relying only on its training data, the model answers based on the company's own documents, procedures or database, which reduces hallucination and gives access to fresh, organization-specific knowledge. A typical architecture has these steps: splitting documents into chunks and turning them into embeddings, storing those vectors in a vector database, retrieving the most relevant chunks via similarity search when a question arrives, and adding that context to the prompt given to the model. RAG is powerful for internal question-answering assistants, customer support bots and search over documents, because it lets the model work with current information without retraining. Data cleanliness, a good chunking strategy and source citation matter for quality. RAG solutions usually rely on API integrations to pull data from systems.