Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors

In this course, you will learn how to build cost-effective Retrieval Augmented Generation (RAG) applications using Amazon Bedrock Knowledge Bases and Amazon S3 Vectors. You'll discover how to reduce vector storage costs by up to 90% while maintaining subsecond query performance for large-scale knowledge bases. This course will guide you through the complete process of integrating Amazon S3 Vectors with Amazon Bedrock Knowledge Bases, from initial setup to testing and deployment. You'll gain hands-on experience with cost-optimized vector storage solutions that scale to handle millions of documents.

• Creating a Bedrock Knowledge Base using Amazon S3 Vectors as the vector store via the AWS Management Console, including configuration of embeddings, chunking strategies, and data sources (e.g., from S3 buckets).
• Ingesting unstructured data (documents, PDFs, etc.), generating vector embeddings, and storing/querying them cost-effectively in S3 Vectors for retrieval in RAG pipelines.
• Performing semantic searches and retrievals from S3 Vectors-backed Knowledge Bases to augment foundation model responses in Amazon Bedrock, with focus on subsecond latency for suitable workloads.
• Applying cost-optimization and best practices, such as evaluating workload fit (e.g., high-volume/low-QPS vs. high-performance needs), monitoring usage, and ensuring security/compliance in vector storage and retrieval.

• Understand how to leverage Amazon S3 Vectors to achieve significant cost reductions (up to 90% on vector upload, storage, and queries) compared to dedicated vector databases, ideal for long-term storage of massive vector datasets in RAG use cases.
• Gain practical knowledge of creating and managing Bedrock Knowledge Bases backed by S3 Vectors, enabling more affordable generative AI applications with preserved retrieval quality and semantic accuracy.
• Be prepared to implement cost-effective RAG solutions in production scenarios, balancing economics with performance requirements and integrating seamlessly with Amazon Bedrock foundation models for enhanced LLM responses.

• Focused digital course content with step-by-step guidance, console walkthroughs, architecture diagrams, and examples demonstrating the S3 Vectors + Bedrock Knowledge Bases integration (aligned with the July 2025 feature launch and AWS blog/tutorials).
• Intermediate-level training in the Artificial Intelligence domain, targeted at developers, ML engineers, and architects building generative AI applications (assumes familiarity with Bedrock basics and RAG concepts).
• Practical coverage of key AWS services (Amazon Bedrock Knowledge Bases, Amazon S3 Vectors for vector storage/query, embedding models) and real-world RAG cost-saving patterns.
• Certificate of completion issued.