Modern Search and Retrieval for AWS Generative AI

As generative AI applications scale, the quality of their outputs depends heavily on the accuracy of the data retrieved for them. To build reliable AI systems, you must understand how to fetch the most relevant information efficiently using cloud tools. This course teaches you how to design and implement robust Retrieval-Augmented Generation (RAG) architectures on AWS. You will transition from basic keyword searches to sophisticated, multi-stage retrieval systems that power modern AI assistants. What you'll learn: - Understand the core concepts of vector databases and semantic search on AWS - Configure hybrid search combining keyword matching with dense vector embeddings - Implement query expansion techniques to better capture user intent - Apply reranking models to prioritize the most relevant search results - Scale your search infrastructure using OpenSearch and OpenSearch Serverless - Evaluate and optimize retrieval accuracy for generative AI workloads You will start by learning the foundational terminology of search, vector embeddings, and RAG architectures. From there, you will read through step-by-step conceptual guides and code examples demonstrating how to orchestrate these components on AWS. This course is designed for software developers, data practitioners, and cloud enthusiasts who are new to generative AI retrieval systems. No prior experience with vector databases or advanced search algorithms is required. Start reading today to elevate your generative AI applications with high-precision AWS retrieval.