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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 Q Business simplifies integration of enterprise knowledge bases at scale

AWS Machine Learning - AI

However, ingesting large volumes of enterprise data poses significant challenges, particularly in orchestrating workflows to gather data from diverse sources. In this post, we propose an end-to-end solution using Amazon Q Business to simplify integration of enterprise knowledge bases at scale.

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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. Choose Next.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning - AI

This is where the integration of cutting-edge technologies, such as audio-to-text translation and large language models (LLMs), holds the potential to revolutionize the way patients receive, process, and act on vital medical information. These insights can include: Potential adverse event detection and reporting.

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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. These indexed documents provide a comprehensive knowledge base that the AI agents consult to inform their responses.

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Introducing guardrails in Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). In the following sections, we demonstrate how to create a knowledge base with guardrails.

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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.