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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

Whether you are developing a customer service chatbot or a virtual assistant, there are numerous considerations to keep in mind, from defining the agent’s scope and capabilities to architecting a robust and scalable infrastructure. Amazon Bedrock Agents has built-in versioning capabilities to help you with this key part of testing.

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Chatbot: Complete Guide

JivoChat

Chatbots have become a success around the world, and nowadays are used by 58% of B2B companies and 42% of B2C companies. In 2022 at least 88% of users had one conversation with chatbots. There are many reasons for that, a chatbot is able to simulate human interaction and provide customer service 24h a day. What Is a Chatbot?

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Build custom generative AI applications powered by Amazon Bedrock

AWS Machine Learning

The wide applicability of LLMs explains why customers across healthcare, financial services, and media and entertainment are moving quickly to adopt them. They recently launched a chatbot solution in beta capable of handling product support queries.

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Customer Service in the Time of COVID-19

ShepHyken

While now may not be the ideal time to launch something new, you should make sure existing tools and programs—knowledge bases, chatbots, communities, and automation, for example—are all operating well and providing up-to-date information. To help entertain housebound kids, Disney released Frozen 2 early on its Disney+ streaming service.

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Integrate QnABot on AWS with ServiceNow

AWS Machine Learning

Conversational AI (or chatbots) can help triage some of these common IT problems and create a ticket for the tasks when human assistance is needed. Chatbots quickly resolve common business issues, improve employee experiences, and free up agents’ time to handle more complex problems.

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Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning

You can build such chatbots following the same process. You can easily build such chatbots following the same process. UI and the Chatbot example application to test human-workflow scenario. In our example, we used a Q&A chatbot for SageMaker as explained in the previous section.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices. Multi-account architecture for sharing models A multi-account strategy improves security, scalability, and reliability of your systems.