RAG and Vector Databases with Upstash: AI Engineering Foundations

As AI applications evolve, static language models are no longer enough to meet user needs. Integrating external knowledge bases through Retrieval-Augmented Generation (RAG) is now an essential skill for modern developers. This written course guides you through the core concepts of RAG, showing you how to store, query, and retrieve contextual data using Upstash Vector. You will transition from understanding basic AI terminology to structuring efficient, context-aware applications. What you'll learn: Understand the foundational concepts of Retrieval-Augmented Generation and vector search; Configure Upstash Vector databases to store and manage high-dimensional data; Compare dense and sparse embeddings to choose the best retrieval strategy for your data; Implement hybrid search techniques to improve retrieval accuracy and relevance; Connect your vector database to language models to generate highly contextual responses; Apply modern RAG patterns to keep your AI applications accurate and reliable. You will start with key terminology and vector database fundamentals before progressing to hands-on configuration, index creation, and advanced query strategies. This course is designed for beginners and aspiring AI engineers who want to learn RAG concepts from scratch, with no prior vector database experience required. Start reading today to build smarter, context-driven AI systems.