RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an AI framework that combines information retrieval with text generation. Instead of relying solely on the knowledge embedded in an LLM's parameters, RAG systems first retrieve relevant documents or data from an external knowledge base, then use this context to generate more accurate and up-to-date responses.

RAG addresses several limitations of pure LLM approaches: it reduces hallucinations by grounding responses in actual documents, enables access to private or recent information not in training data, and provides transparency through source attribution.

A typical RAG pipeline involves: (1) embedding documents into a vector database, (2) converting user queries into embeddings, (3) performing similarity search to find relevant chunks, (4) combining retrieved context with the user query, and (5) generating a response using an LLM. This architecture is fundamental to building knowledge-based AI applications like customer support bots, documentation assistants, and enterprise search systems.