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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

For instance, consider an AI-driven legal document analysis system designed for businesses of varying sizes, offering two primary subscription tiers: Basic and Pro. Based on the classifier LLMs decision, the Lambda function routes the question to the appropriate downstream LLM, which will generate an answer and return it to the user.

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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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Knowledge Bases for Amazon Bedrock now supports advanced parsing, chunking, and query reformulation giving greater control of accuracy in RAG based applications

AWS Machine Learning - AI

Now that you understand the concepts for semantic and hierarchical chunking, in case you want to have more flexibility, you can use a Lambda function for adding custom processing logic to chunks such as metadata processing or defining your custom logic for chunking. Make sure to create the Lambda layer for the specific open source framework.

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How Mixbook used generative AI to offer personalized photo book experiences

AWS Machine Learning - AI

The raw photos are stored in Amazon Simple Storage Service (Amazon S3). Aurora MySQL serves as the primary relational data storage solution for tracking and recording media file upload sessions and their accompanying metadata. S3, in turn, provides efficient, scalable, and secure storage for the media file objects themselves.

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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. Amazon Simple Storage Service (S3) : for documents and processed data caching.

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High-performance computing on AWS

Xebia

Key features of AWS Batch Efficient Resource Management: AWS Batch automatically provisions the required resources, such as compute instances and storage, based on job requirements. Integration with AWS Services: AWS Batch seamlessly integrates with other AWS services, such as Amazon S3, AWS Lambda, and Amazon DynamoDB.

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

AWS Machine Learning - AI

Solution overview The policy documents reside in Amazon Simple Storage Service (Amazon S3) storage. 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.