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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

An example is a virtual assistant for enterprise business operations. For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. The architecture of this system is illustrated in the following figure.

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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

Solution overview This section outlines the architecture designed for an email support system using generative AI. High Level System Design The solution consists of the following components: Email service – This component manages incoming and outgoing customer emails, serving as the primary interface for email communications.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

Much like traditional business process automation through technology, the agentic AI architecture is the design of AI systems designed to resolve complex problems with limited or indirect human intervention. New agents can be added to handle specific types of messages without changing the overall system architecture.

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Medical content creation in the age of generative AI

AWS Machine Learning - AI

The solution has been designed using the following services: Amazon Elastic Container Service (ECS) : to deploy and manage our Streamlit UI. Amazon Lambda : to run the backend code, which encompasses the generative logic. In step 5, the lambda function triggers the Amazon Textract to parse and extract data from pdf documents.

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

AWS Machine Learning - AI

During the solution design process, Verisk also considered using Amazon Bedrock Knowledge Bases because its purpose built for creating and storing embeddings within Amazon OpenSearch Serverless. In the future, Verisk intends to use the Amazon Titan Embeddings V2 model. The user can pick the two documents that they want to compare.

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

AWS Machine Learning - AI

Generative AI question-answering applications are pushing the boundaries of enterprise productivity. In this post, we discuss best practices for applying LLMs to generate ground truth for evaluating question-answering assistants with FMEval on an enterprise scale.

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Journey to Event Driven – Part 3: The Affinity Between Events, Streams and Serverless

Confluent

Given that it is at a relatively early stage, developers are still trying to grok the best approach for each cloud vendor and often face the following question: Should I go cloud native with AWS Lambda, GCP functions, etc., The key to event-first systems design is understanding that a series of events captures behavior.