Remove Knowledge Base Remove Metrics Remove Training
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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

Demystifying RAG and model customization RAG is a technique to enhance the capability of pre-trained models by allowing the model access to external domain-specific data sources. It combines two components: retrieval of external knowledge and generation of responses. To do so, we create a knowledge base.

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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning - AI

For automatic model evaluation jobs, you can either use built-in datasets across three predefined metrics (accuracy, robustness, toxicity) or bring your own datasets. Regular evaluations allow you to adjust and steer the AI’s behavior based on feedback and performance metrics.

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Elevate RAG for numerical analysis using Amazon Bedrock Knowledge Bases

AWS Machine Learning - AI

Amazon Bedrock Knowledge Bases is a fully managed capability that helps you implement the entire RAG workflow—from ingestion to retrieval and prompt augmentation—without having to build custom integrations to data sources and manage data flows. Latest innovations in Amazon Bedrock Knowledge Base provide a resolution to this issue.

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What to expect from AI in the enterprise in 2025

CIO

In this scenario, using AI to improve employee capabilities by building on the existing knowledge base will be key. Foundation models (FMs) by design are trained on a wide range of data scraped and sourced from multiple public sources. Failure to do so could mean a 500% to 1,000% error increase in their cost calculations.

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Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning - AI

FMs are trained on vast quantities of data, allowing them to be used to answer questions on a variety of subjects. Knowledge Bases for Amazon Bedrock is a fully managed RAG capability that allows you to customize FM responses with contextual and relevant company data. The following diagram depicts a high-level RAG architecture.

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Boost team productivity with Amazon Q Business Insights

AWS Machine Learning - AI

Your data is not used for training purposes, and the answers provided by Amazon Q Business are based solely on the data users have access to. By monitoring utilization metrics, organizations can quantify the actual productivity gains achieved with Amazon Q Business.

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7 ways gen AI can create more work than it saves

CIO

Managers tend to incentivize activity metrics and measure inputs versus outputs,” she adds. Or instead of writing one article for the company knowledge base on a topic that matters most to them, they might submit a dozen articles, on less worthwhile topics. You need people who are trained to see that.