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Guest Post: 5 Contact Center Best Practices That Make Clients Happier

ShepHyken

Following best practices and applying hacks has proven fruitful for a lot of contact centers. This shortlist of contact center best practice hacks should set you up for success in the pursuit of customer service excellence. You can do the same and improve your customers’ experience. At least for now!

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Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

Amazon Bedrock empowers teams to generate Terraform and CloudFormation scripts that are custom fitted to organizational needs while seamlessly integrating compliance and security best practices. This makes sure your cloud foundation is built according to AWS best practices from the start.

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

AWS Machine Learning

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. It’s also a best practice to collect any extra information that would be shared with the agent in a production system. Implement citation mechanisms to reference source documents in responses.

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20 Car Sales Scripts to Close More Deals

JustCall

Effective sales scripts tailored for various customer interactions can guide conversations to successful outcomes. This article provides ten car sales script samples that harness the power of persuasive dialogue to boost your sales performance with JustCall. 20 Car Sales Scripts Need to rev up your sales conversations?

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning

In this post, we dive into tips and best practices for successful LLM training on Amazon SageMaker Training. The post covers all the phases of an LLM training workload and describes associated infrastructure features and best practices. Some of the best practices in this post refer specifically to ml.p4d.24xlarge

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Best practices for load testing Amazon SageMaker real-time inference endpoints

AWS Machine Learning

This post describes the best practices for load testing a SageMaker endpoint to find the right configuration for the number of instances and size. Note that the model container also includes any custom inference code or scripts that you have passed for inference. The Locust file we’re using is locust_script.py.

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Best practices to build generative AI applications on AWS

AWS Machine Learning

Model customization has higher complexity than prompt engineering and RAG because the model’s weight and parameters are being changed via tuning scripts, which requires data science and ML expertise. This optimization of the model for its intended use allows you to deploy FMs successfully in real applications.