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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. The code and resources required for deployment are available in the amazon-bedrock-examples repository.

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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

Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. This is achieved by writing Terraform code within an application-specific repository.

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Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning - AI

The entire conversation in this use case, starting with generative AI and then bringing in human agents who take over, is logged so that the interaction can be used as part of the knowledge base. We also have another expert group providing feedback using Amazon SageMaker GroundTruth on completion quality for the RLHF based training.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning - AI

They also allow for simpler application layer code because the routing logic, vectorization, and memory is fully managed. It uses the provided conversation history, action groups, and knowledge bases to understand the context and determine the necessary tasks. This text input is captured and sent to the AI assistant.

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

AWS Machine Learning - AI

Solution code and deployment assets can be found in the GitHub repository. Amazon Lex then invokes an AWS Lambda handler for user intent fulfillment. The Lambda function associated with the Amazon Lex chatbot contains the logic and business rules required to process the user’s intent.

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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.