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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning - AI

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle.

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Amazon Bedrock Marketplace now includes NVIDIA models: Introducing NVIDIA Nemotron-4 NIM microservices

AWS Machine Learning - AI

Organizations can use these models securely, and for models that are compatible with the Amazon Bedrock Converse API, you can use the robust toolkit of Amazon Bedrock, including Amazon Bedrock Agents , Amazon Bedrock Knowledge Bases , Amazon Bedrock Guardrails , and Amazon Bedrock Flows. You can find him on LinkedIn.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

The introduction of Amazon Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with Amazon Nova models as a baseline.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices. This scalability allows for more frequent and comprehensive reviews.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Generative artificial intelligence (AI) has gained significant momentum with organizations actively exploring its potential applications. As successful proof-of-concepts transition into production, organizations are increasingly in need of enterprise scalable solutions.

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Build a gen AI–powered financial assistant with Amazon Bedrock multi-agent collaboration

AWS Machine Learning - AI

The use of a multi-agent system, rather than relying on a single large language model (LLM) to handle all tasks, enables more focused and in-depth analysis in specialized areas. The primary agent can also consult attached knowledge bases or trigger action groups before or after subagent involvement.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. Which LLM you want to use in Amazon Bedrock for text generation.