In this chapter, we’re going to introduce you to a game-changing technique called retrieval-augmented generation (RAG), an outcome of the work carried out by researchers at Facebook AI (now Meta). It’s the secret sauce that empowers language models such as GPT to bridge the gap between their static knowledge and the dynamic real world. With RAG, we’ll show you how to equip your generative AI applications with the ability to pull in fresh information, ground your organizational data, cross-reference facts to address hallucinations, and stay contextually aware, all in real time. We will also discuss the fundaments of vector databases, a new, hot, and emerging database that is designed for storing, indexing, and querying vectors that represent highly dimensional data; they are typically used for similarity search and machine learning applications and are important in building RAG applications.