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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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5 Top Customer Service Articles for the Week of April 1, 2019

ShepHyken

AI adoption is nascent, but it’s set to soar as more teams turn to chatbots, text, and voice analytics, and other use cases. Search Engine Journal) In this article, we’ll go through all the steps of building a social customer service strategy from scratch and answer the frequently asked questions about social customer support.

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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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Vitech uses Amazon Bedrock to revolutionize information access with AI-powered chatbot

AWS Machine Learning

Vitech helps group insurance, pension fund administration, and investment clients expand their offerings and capabilities, streamline their operations, and gain analytical insights. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system. Prompts also help ground the model.

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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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Cutting Through the Buzzwords of AI in the Contact Center

CCNG

There are many types of AI, however, 95% of AI is being utilized effectively and most of the innovation in the contact center is based on Generative and Analytical. Analytical AI analyzes large amounts of data and processes quickly, sometimes in real-time, and creates actionable insights from that data.

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Chatbot Analytics: Essential Metrics & KPIs to Measure Bot Success

REVE Chat Blog

Chatbots are not something that you can just “set and forget”. Building a good chatbot is a daunting task but at the same time, it is important to understand the key chatbot metrics and how they are performing to achieve your goals. Simply automating business tasks with an AI chatbot isn’t enough. Gartner Research).