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Faster, Better, Cheaper: How to Measure the Business Impact of LLMs

Xebia

In the whitepaper How to Prioritize LLM Use Cases , we show that LLMs may not always outperform human expertise, but they offer a competitive advantage when tasks require quick execution and scalable automation. Additionally, LLMs can power internal knowledge management systems, helping employees find information quickly.

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

Verisk has a governance council that reviews generative AI solutions to make sure that they meet Verisks standards of security, compliance, and data use. Verisk also has a legal review for IP protection and compliance within their contracts. This enables Verisks customers to cut the change adoption time from days to minutes.

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EBSCOlearning scales assessment generation for their online learning content with generative AI

AWS Machine Learning - AI

Enter AI: A promising solution Recognizing the potential of AI to address this challenge, EBSCOlearning partnered with the GenAIIC to develop an AI-powered question generation system. This multifaceted approach makes sure that the questions adhere to all quality standards and guidelines. Sonnet in Amazon Bedrock.

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How BQA streamlines education quality reporting using Amazon Bedrock

AWS Machine Learning - AI

Manually reviewing and processing this information can be a challenging and time-consuming task, with a margin for potential errors. BQA reviews the performance of all education and training institutions, including schools, universities, and vocational institutes, thereby promoting the professional advancement of the nations human capital.

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Why GreenOps will succeed where FinOps is failing

CIO

This surge is driven by the rapid expansion of cloud computing and artificial intelligence, both of which are reshaping industries and enabling unprecedented scalability and innovation. Capital One built Cloud Custodian initially to address the issue of dev/test systems left running with little utilization. Short-term focus.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

Ground truth data in AI refers to data that is known to be factual, representing the expected use case outcome for the system being modeled. By providing an expected outcome to measure against, ground truth data unlocks the ability to deterministically evaluate system quality.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

In this post, we seek to address this growing need by offering clear, actionable guidelines and best practices on when to use each approach, helping you make informed decisions that align with your unique requirements and objectives. On the Review and create page, review the settings and choose Create Knowledge Base.