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Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

AWS Machine Learning Blog

This post focuses on RAG evaluation with Amazon Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. Jesse Manders is a Senior Product Manager on Amazon Bedrock, the AWS Generative AI developer service.

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How can AI and data protection work together: unveiling the truth

Pickl AI

We will navigate the landscape of AI’s ascendancy and underscore the pivotal role of integrating AI with robust data protection measures. Understanding AI privacy issues In delving into the intricate realm of AI privacy issues, real-world examples vividly illuminate the challenges.

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What is Data-Centric Architecture in AI?

Pickl AI

By leveraging comprehensive and accurate data, it provides more informed and evidence-based decisions, leading to improved outcomes across various domains. Ethical and Responsible AI Data-centric AI architecture promotes ethical practices and responsible AI development.

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Democratizing AI: Exploring the Impact of Low/No-Code AI Development Tools

Unite.AI

From powering recommendation algorithms on streaming platforms to enabling autonomous vehicles and enhancing medical diagnostics, AI's ability to analyze vast amounts of data, recognize patterns, and make informed decisions has transformed fields like healthcare, finance, retail, and manufacturing.

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What is AI risk management?

IBM Journey to AI blog

AI governance establishes the frameworks, rules and standards that direct AI research, development and application to ensure safety, fairness and respect for human rights. Learn how IBM Consulting can help weave responsible AI governance into the fabric of your business. The framework is divided into two parts.

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AI TRiSM: A Framework for Trustworthy AI Systems

Pickl AI

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.