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Improving Retrieval Augmented Generation accuracy with GraphRAG

AWS Machine Learning Blog

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. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

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Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock. When you send a message to a model, you can provide definitions for one or more tools that could potentially help the model generate a response.

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Develop and train large models cost-efficiently with Metaflow and AWS Trainium

AWS Machine Learning Blog

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace

AWS Machine Learning Blog

We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace. Additionally, you can securely integrate and easily deploy your generative AI applications using the AWS tools you are already familiar with.

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Using natural language in Amazon Q Business: From searching and creating ServiceNow incidents and knowledge articles to generating insights

AWS Machine Learning Blog

Prerequisites Before proceeding, make sure that you have the necessary AWS account permissions and services enabled, along with access to a ServiceNow environment with the required privileges for configuration. AWS Have an AWS account with administrative access. For AWS Secrets Manager secret, choose Create and add a new secret.

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Automate building guardrails for Amazon Bedrock using test-driven development

AWS Machine Learning Blog

Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. Make sure that you have the correct AWS Identity and Access Management (IAM) permissions to use Amazon Bedrock. Have access to the large language model (LLM) that will be used.

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How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

AWS Machine Learning Blog

The AML feature store standardizes variable definitions using scientifically validated algorithms. Discover and its transactional and batch applications are deployed and scaled on a Kubernetes on AWS cluster to optimize performance, user experience, and portability. The following diagram illustrates the solution architecture.