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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The Amazon D&C team implemented the solution in a pilot for Amazon engineers and collected user feedback. During the pilot, users provided 118 feedback responses.

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Lesson #24 Revisited: Shorter surveys are usually better—but if it’s too short, it could come back to bite you

PeopleMetrics

Here are three major problems with relying on overly short surveys: Lack of Granularity: With only one open-ended "why" question, you're banking on customers to spontaneously provide detailed feedback about specific parts of their experience. Text analytics tools are impressive and improving rapidly, but they're not foolproof.

Surveys 117
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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning

QnABot is a multilanguage, multichannel conversational interface (chatbot) that responds to customers’ questions, answers, and feedback. Usability and continual improvement were top priorities, and Principal enhanced the standard user feedback from QnABot to gain input from end-users on answer accuracy, outdated content, and relevance.

Chatbots 115
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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Feedback loop implementation: Create a mechanism to continuously update the verified cache with new, accurate responses. About the Authors Dheer Toprani is a System Development Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team.

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The Challenges of Omnichannel: Why so Many Contact Centers Struggle with Digital Self-Service

To find how contact centers are navigating the transition to omnichannel customer service, Calabrio surveyed more than 1,000 marketing and customer experience leaders in the U.S. about their digital customer communication strategies. Read the report to find out what was uncovered.

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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

The initial draft of a large language model (LLM) generated earnings call script can be then refined and customized using feedback from the company’s executives. Maintain a measured, objective, and analytical tone throughout the content, avoiding overly conversational or casual language.

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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Observability empowers you to proactively monitor and analyze your generative AI applications, and evaluation helps you collect feedback, refine models, and enhance output quality. In the context of Amazon Bedrock , observability and evaluation become even more crucial.

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.