Remove AWS Remove Information Remove Natural Language Processing
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Implement RAG while meeting data residency requirements using AWS hybrid and edge services

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In this post, we show how to extend Amazon Bedrock Agents to hybrid and edge services such as AWS Outposts and AWS Local Zones to build distributed Retrieval Augmented Generation (RAG) applications with on-premises data for improved model outcomes.

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Transcribe, translate, and summarize live streams in your browser with AWS AI and generative AI services

AWS Machine Learning Blog

However, as the reach of live streams expands globally, language barriers and accessibility challenges have emerged, limiting the ability of viewers to fully comprehend and participate in these immersive experiences. The extension delivers a web application implemented using the AWS SDK for JavaScript and the AWS Amplify JavaScript library.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning Blog

This wealth of content provides an opportunity to streamline access to information in a compliant and responsible way. Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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Amazon Q Business , a new generative AI-powered assistant, can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in an enterprises systems. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive.

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Improving Retrieval Augmented Generation accuracy with GraphRAG

AWS Machine Learning Blog

In a world where decisions are increasingly data-driven, the integrity and reliability of information are paramount. Lettria , an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods.

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Intelligent healthcare assistants: Empowering stakeholders with personalized support and data-driven insights

AWS Machine Learning Blog

Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

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By narrowing down the search space to the most relevant documents or chunks, metadata filtering reduces noise and irrelevant information, enabling the LLM to focus on the most relevant content. This approach can also enhance the quality of retrieved information and responses generated by the RAG applications.

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