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Best practices for building secure applications with Amazon Transcribe

AWS Machine Learning

Amazon Transcribe can be used for transcription of customer care calls, multiparty conference calls, and voicemail messages, as well as subtitle generation for recorded and live videos, to name just a few examples. Applications must have valid credentials to sign API requests to AWS services.

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Knowledge Bases for Amazon Bedrock now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

AWS Machine Learning

In this post, we discuss two new features of Knowledge Bases for Amazon Bedrock specific to the RetrieveAndGenerate API: configuring the maximum number of results and creating custom prompts with a knowledge base prompt template. Additionally, you can add custom instructions and examples tailored to your specific workflows.

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Drive efficiencies with CI/CD best practices on Amazon Lex

AWS Machine Learning

You liked the overall experience and now want to deploy the bot in your production environment, but aren’t sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations.

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Get more out of SuccessBLOCS and APIs

Totango

Earlier this year we launched the SuccessBLOC marketplace to make finding best practices and templates easier. Stream Account & User Tag Information Using Customer Data Hub API. Now, tag information can be easily streamed to Totango via Customer Data Hub API. Save your spot . Have a wonderful safe week, Ravit .

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5 Text Messaging Best Practices for Your SMS Strategy

aircall

With best practices for text messaging, your customer-facing teams will provide better service, while your sales and marketing teams can better interact with leads on a more personalized scale to more efficiently close deals. Why Use Text Messaging Best Practices. SMS Marketing: Text Messaging Marketing Best Practices.

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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. You can choose from various FMs from Amazon and leading AI startups such as AI21 Labs, Anthropic, Cohere, and Stability AI to find the model that’s best suited for your use case.

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Build a foundation model (FM) powered customer service bot with agents for Amazon Bedrock

AWS Machine Learning

The structured prompts include a sequence of question-thought-action-observation examples. The action is an API that the model can invoke from an allowed set of APIs. Action groups are mapped to an AWS Lambda function and related API schema to perform API calls. The following diagram depicts the agent structure.

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