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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime ) to a centralized video insights and summarization engine. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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Build generative AI chatbots using prompt engineering with Amazon Redshift and Amazon Bedrock

AWS Machine Learning

It enables you to privately customize the FMs with your data using techniques such as fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG), and build agents that run tasks using your enterprise systems and data sources while complying with security and privacy requirements.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. Now, employees at Principal can receive role-based answers in real time through a conversational chatbot interface.

Chatbots 114
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Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex

AWS Machine Learning

These include interactive voice response (IVR) systems, chatbots for digital channels, and messaging platforms, providing a seamless and resilient customer experience. Yogesh Khemka is a Senior Software Development Engineer at AWS, where he works on large language models and natural language processing.

Chatbots 113
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The Challenges of Omnichannel: Why so Many Contact Centers Struggle with Digital Self-Service

To find how contact centers are navigating the transition to omnichannel customer service, Calabrio surveyed more than 1,000 marketing and customer experience leaders in the U.S. about their digital customer communication strategies. Read the report to find out what was uncovered.

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How IDIADA optimized its intelligent chatbot with Amazon Bedrock

AWS Machine Learning

In 2021, Applus+ IDIADA , a global partner to the automotive industry with over 30 years of experience supporting customers in product development activities through design, engineering, testing, and homologation services, established the Digital Solutions department.

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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning

In this post, we explore building a contextual chatbot for financial services organizations using a RAG architecture with the Llama 2 foundation model and the Hugging Face GPTJ-6B-FP16 embeddings model, both available in SageMaker JumpStart. The following diagram shows the conceptual flow of using RAG with LLMs.