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Empower your generative AI application with a comprehensive custom observability solution

AWS Machine Learning

Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. Observability empowers you to proactively monitor and analyze your generative AI applications, and evaluation helps you collect feedback, refine models, and enhance output quality.

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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

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. QnABot is a multilanguage, multichannel conversational interface (chatbot) that responds to customers’ questions, answers, and feedback.

Chatbots 113
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Guest Post: A Guide to Training Employees to Deliver Outstanding eCommerce Customer Service

ShepHyken

The vision document is critical to set the direction for your team, so you need to make it clear. Also, make it available at all times through your company’s document sharing service. Include an explanation of each touchpoint in a separate document. Consider these meetings to keep the feedback flowing: Weekly group meetings.

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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

Current RAG pipelines frequently employ similarity-based metrics such as ROUGE , BLEU , and BERTScore to assess the quality of the generated responses, which is essential for refining and enhancing the models capabilities. More sophisticated metrics are needed to evaluate factual alignment and accuracy.

Metrics 84
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Study: The Health of the Contact Center

What does it take to engage agents in this customer-centric era? Download our study of 1,000 contact center agents in the US and UK to find out what major challenges are facing contact center agents today – and what your company can do about it.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Rigorous testing allows us to understand an LLMs capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Evaluation algorithm Computes evaluation metrics to model outputs. Different algorithms have different metrics to be specified.

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning

Continuous fine-tuning also enables models to integrate human feedback, address errors, and tailor to real-world applications. When you have user feedback to the model responses, you can also use reinforcement learning from human feedback (RLHF) to guide the LLMs response by rewarding the outputs that align with human preferences.

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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.

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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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The Health of the Contact Center: Are You Ready for 2019?

A survey of 1,000 contact center professionals reveals what it takes to improve agent well-being in a customer-centric era. This report is a must-read for contact center leaders preparing to engage agents and improve customer experience in 2019.