Generative AI Performance Optimization with OpenSearch and Bedrock

As generative AI applications scale, keeping retrieval latency low and search results highly relevant becomes a critical operational challenge. This course guides you through the foundational concepts and practical strategies needed to optimize your AI-powered search and retrieval systems. By reading through this comprehensive text-based guide, you will transition from building basic AI prototypes to designing highly efficient, production-ready retrieval systems. You will understand how to balance speed, cost, and accuracy using modern search technologies and cloud tools. What you'll learn: - Understand the core concepts of vector databases, embeddings, and hybrid search retrieval - Tune OpenSearch index settings to significantly reduce search latency - Configure hybrid search queries that combine keyword matching with semantic vector search - Implement Bedrock model configurations to balance response speed and generation quality - Apply tracing and diagnostic strategies to identify and resolve performance bottlenecks - Design efficient retrieval-augmented generation patterns for real-world applications The course begins with essential terminology, outlining the mechanics of semantic search and modern AI architectures. You will then progress through step-by-step written explanations of query tuning, index optimization, and performance tracing. This course is designed for developers, system architects, and technical enthusiasts who are new to AI operations. No advanced background in machine learning is required to get started. Start reading today to build faster, more efficient generative AI applications.