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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

You can now use Agents for Amazon Bedrock and Knowledge Bases for Amazon Bedrock to configure specialized agents that seamlessly run actions based on natural language input and your organization’s data. System integration – Agents make API calls to integrated company systems to run specific actions.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning - AI

With Amazon Bedrock, teams can input high-level architectural descriptions and use generative AI to generate a baseline configuration of Terraform scripts. AWS Landing Zone architecture in the context of cloud migration AWS Landing Zone can help you set up a secure, multi-account AWS environment based on AWS best practices.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

With AWS generative AI services like Amazon Bedrock , developers can create systems that expertly manage and respond to user requests. An AI assistant is an intelligent system that understands natural language queries and interacts with various tools, data sources, and APIs to perform tasks or retrieve information on behalf of the user.

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Build generative AI agents with Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain

AWS Machine Learning - AI

This solution is intended to act as a launchpad for developers to create their own personalized conversational agents for various applications, such as virtual workers and customer support systems. Solution architecture The following diagram illustrates the solution architecture.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning - AI

To create AI assistants that are capable of having discussions grounded in specialized enterprise knowledge, we need to connect these powerful but generic LLMs to internal knowledge bases of documents. In Part 1, we review the RAG design pattern and its limitations on analytical questions.

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Welcome to a New Era of Building in the Cloud with Generative AI on AWS

AWS Machine Learning - AI

SageMaker also actively monitors instances that are processing inference requests and intelligently routes requests based on which instances are available—achieving 20% lower inference latency (on average). Conjecture, Salesforce, and Slack are already using SageMaker for hosting models due to these inference optimizations.