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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.

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Medical content creation in the age of generative AI

AWS Machine Learning - AI

Generative AI and transformer-based large language models (LLMs) have been in the top headlines recently. These models demonstrate impressive performance in question answering, text summarization, code, and text generation. Amazon Lambda : to run the backend code, which encompasses the generative logic.

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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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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. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

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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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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.