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Deliver relevant, accurate, and customized responses with RAG and Amazon Bedrock

Generative AI applications can deliver better responses by incorporating organization-specific data with Retrieval Augmented Generation (RAG). However, implementing RAG requires specific skillset and time to configure connections to data sources, manage data ingestion workflows, and write custom code to manage the interactions between the foundation model (FM) and the data sources. This session covers how you can simplify the process with Amazon Bedrock knowledge bases and agents. From the user prompt, Amazon Bedrock automatically identifies data sources, retrieves the relevant information, and adds them to the prompt, thereby giving the FM more information to generate responses. At the end of this session, understand how these tools enable you to deliver more relevant, accurate, and customized responses based on your organization’s proprietary knowledge sources.

Speaker: Xin Chen, Senior Cloud Architect, AWS Professional Services