Large Language Models (LLMs) are revolutionary, but they often struggle with domain-specific, proprietary, or real-time information. Retrieval-Augmented Generation (RAG) offers a powerful solution, allowing LLMs to leverage external knowledge. This article dives into building robust AI knowledge base applications using RAG, with a focus on integrating pgvector for efficient, scalable vector storage and similarity search.