Remove 2022 Remove Chatbots Remove Document Remove Technology
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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly as the user base grows; therefore, they present a cost-effective solution for engaging customers.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.

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100+ Customer Experience Stats to Prepare for 2023

CCNG

Gartner, 2018) According to customers, in 2022, only 3% of companies are customer-obsessed — putting customers at the center of their leadership, strategy, and operations — a decrease of 7 percentage points from the prior year. Forrester, 2022) 80% of CEOs believe they deliver superior customer experience. Qualtrics, 2022).

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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning

It enables searching over both the content of documents and their underlying meaning. For example, if you have want to build a chatbot for an ecommerce website to handle customer queries such as the return policy or details of the product, using hybrid search will be most suitable.

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Choosing your Customer Communication Channels in 2022

Quadient

Choosing your Customer Communication Channels in 2022. Tue, 07/26/2022 - 16:40. Business-critical documents, such as letters, invoices, and contracts, were sent and received through physical mail. Chatbots are similar to live chat, but your customers chat with a bot instead of a live person on your website. j.shah-thiel.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning

If you’re implementing complex RAG applications into your daily tasks, you may encounter common challenges with your RAG systems such as inaccurate retrieval, increasing size and complexity of documents, and overflow of context, which can significantly impact the quality and reliability of generated answers.

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The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning

Given the data sources, LLMs provided tools that would allow us to build a Q&A chatbot in weeks, rather than what may have taken years previously, and likely with worse performance. To enable quick information retrieval, we use Amazon Kendra as the index for these documents.