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Using transcription confidence scores to improve slot filling in Amazon Lex

AWS Machine Learning Blog

Customer: Id like to check my booking. Virtual Agent: Thats great, please say your 5 character booking reference, you will find it at the top of the information pack we sent. What is your booking reference? Virtual Agent: Your booking 1 9 A A B is currently being progressed. Customer: Id like to check my booking.

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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

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Syngenta and AWS collaborated to develop Cropwise AI , an innovative solution powered by Amazon Bedrock Agents , to accelerate their sales reps’ ability to place Syngenta seed products with growers across North America. The collaboration between Syngenta and AWS showcases the transformative power of LLMs and AI agents.

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Improving Retrieval Augmented Generation accuracy with GraphRAG

AWS Machine Learning Blog

Lettria , an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

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Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex

AWS Machine Learning Blog

Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. We showcase the replication process of bot versions and aliases across multiple Regions. Solution overview For this exercise, we create a BookHotel bot as our sample bot.

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5 Top Large Language Models & Generative AI Books

Towards AI

Master LLMs & Generative AI Through These Five Books This article reviews five key books that explore the rapidly evolving fields of large language models (LLMs) and generative AI, providing essential insights into these transformative technologies.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

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Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning Blog

This solution uses decorators in your application code to capture and log metadata such as input prompts, output results, run time, and custom metadata, offering enhanced security, ease of use, flexibility, and integration with native AWS services.

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