Category: Amazon Bedrock Knowledge Bases

Organizations generate massive volumes of structured data from customer transactions, operational metrics, product catalogs, and compliance records. This data contains insights that can help businesses make better and timely decisions. Financial advisors need to review client transaction histories, retail analysts track inventory trends, and healthcare administrators monitor patient outcomes. Yet accessing these insights creates a […]

Since its launch in 2006, Amazon S3 has used a global namespace where bucket names must be unique across all AWS accounts and AWS Regions. This design has served customers well at scale, but organizations managing multiple accounts and environments often encounter naming collisions. When a bucket is deleted, its name returns to the global […]

State-of-the-art AI agents rely on external tools to perform actions on their behalf. A tool is a function with a clear description, defined inputs, and outputs that extend the capabilities of a large language model (LLM). As toolkits expand, selecting the right tool for each task requires effective mechanisms, among which semantic search enables agents […]

Retrieval-Augmented Generation (RAG) is a technique that optimizes large language model (LLM) outputs by referencing authoritative knowledge bases outside of the model’s training data before generating responses. This addresses common limitations of traditional LLMs, such as outdated knowledge, hallucinated facts, and misinterpreted terminology. Organizations can implement RAG to enhance their generative AI applications with current, […]