Vector Stores and Embeddings in AWS: Building Semantic Search

As generative AI applications scale, traditional keyword search is no longer enough to deliver relevant results. Understanding how to work with vector embeddings is the key to building intelligent, context-aware retrieval systems on AWS. This text-only course guides you through the foundational concepts of vector databases and semantic search within the AWS ecosystem. You will learn how to represent text as high-dimensional vectors, store them efficiently, and retrieve them to power modern generative AI patterns like Retrieval-Augmented Generation (RAG). What you'll learn: 1. Understand the core concepts of vector embeddings and semantic similarity metrics. 2. Configure and manage vector stores using OpenSearch Service and Aurora PostgreSQL. 3. Implement Retrieval-Augmented Generation (RAG) patterns to connect LLMs with your private data. 4. Store and organize raw data assets securely using S3 as a foundational data lake. 5. Apply modern best practices for vector indexing, query optimization, and cost-effective scaling. You will start with key terminology and the mathematical intuition behind embeddings before reading through step-by-step explanations for configuring AWS database services and executing semantic queries. This course is designed for software developers, cloud enthusiasts, and data professionals who are new to vector databases and want to build modern AI-driven search solutions on AWS. No prior experience with generative AI is required. Start reading today to unlock the power of semantic search in the cloud.