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Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning - AI

In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents , Amazon Bedrock Knowledge Bases , and Amazon Bedrock Guardrails. Solution overview This section outlines the architecture designed for an email support system using generative AI.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

In this post, we describe the development journey of the generative AI companion for Mozart, the data, the architecture, and the evaluation of the pipeline. Data: Policy forms Mozart is designed to author policy forms like coverage and endorsements. The following diagram illustrates the solution architecture.

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Create a generative AI-based application builder assistant using Amazon Bedrock Agents

AWS Machine Learning - AI

They use the developer-provided instruction to create an orchestration plan and then carry out the plan by invoking company APIs and accessing knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to the end user. We use Amazon Bedrock Agents with two knowledge bases for this assistant.

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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

AWS Machine Learning - AI

The following diagram illustrates our solution architecture. Solutions architecture The workflow includes the following steps: The client profile is stored as key-value pairs in JSON format. All the information retrieved from Amazon Bedrock Knowledge Bases is provided with citations to improve transparency and increase accuracy.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. Generative AI question-answering applications are pushing the boundaries of enterprise productivity.

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Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG

CIO

Choosing the right data architecture Currently, there are two primary technologies that are used to organize the data and the context needed for a RAG framework to generate accurate, relevant responses: Vector Databases (DBs) and Knowledge Graphs.

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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning - AI

Our internal AI sales assistant, powered by Amazon Q Business , will be available across every modality and seamlessly integrate with systems such as internal knowledge bases, customer relationship management (CRM), and more. Dynamic templates – Adapt prompt templates based on retrieved customer information.