RAG Architecture

RAG (Retrieval-Augmented Generation) is the architecture pattern for building AI applications that answer questions using your own data. It combines vector databases, embedding models, and LLMs to create chatbots, search engines, and knowledge assistants that are grounded in specific, up-to-date information. RAG solves LLM hallucination and knowledge cutoff problems by retrieving relevant context before generating answers. It's the most in-demand AI engineering skill as every company wants AI features powered by their own data.