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Delight your customers with great conversational experiences via QnABot, a generative AI chatbot

AWS Machine Learning

In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences. After authentication, Amazon API Gateway and Amazon S3 deliver the contents of the Content Designer UI.

Chatbots 109
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What Timeframe for an AI Chatbot Project?

Inbenta

Depending on the context in which the chatbot project takes place, and therefore its scope of action, its implementation may take more or less time. Indeed, the development of a chatbot implies creating new jobs such as the one of Botmaster for example. How long does it take to deploy an AI chatbot? Let’s see what these can be.

Chatbots 140
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Building AI chatbots using Amazon Lex and Amazon Kendra for filtering query results based on user context

AWS Machine Learning

When the user signs in to an Amazon Lex chatbot, user context information can be derived from Amazon Cognito. The Amazon Lex chatbot can be integrated into Amazon Kendra using a direct integration or via an AWS Lambda function. The use of the AWS Lambda function will provide you with fine-grained control of the Amazon Kendra API calls.

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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning

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. Contextual-based chatbots – Conversations can rapidly change direction and cover unpredictable topics.

APIs 115
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Elevate customer experience through an intelligent email automation solution using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. If the intent doesn’t have a match, the email goes to the support team for a manual response.

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Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy

AWS Machine Learning

The following are common use cases for metadata filtering: Document chatbot for a software company – This allows users to find product information and troubleshooting guides. Filters on the release version, document type (such as code, API reference, or issue) can help pinpoint relevant documents. Virginia) and US West (Oregon).

APIs 111
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Deploy self-service question answering with the QnABot on AWS solution powered by Amazon Lex with Amazon Kendra and large language models

AWS Machine Learning

Powered by Amazon Lex , the QnABot on AWS solution is an open-source, multi-channel, multi-language conversational chatbot. This includes automatically generating accurate answers from existing company documents and knowledge bases, and making their self-service chatbots more conversational. For example, when asked “What is Amazon Lex?”,