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How to Prevent Microsoft Copilot Hallucinations

Microsoft Copilot is a powerful AI assistant that helps you streamline tasks and boost your productivity. However, like all generative AI, it occasionally produces “hallucinations,” which are responses that sound confident but may be factually incorrect.  In fact, some studies suggest that...

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How to Prepare Data for Microsoft 365 Copilot

Microsoft Copilot is changing the game for teams looking to get more out of their data. But to really see its full potential, your data needs to be prepared thoughtfully—organized, clean, and secure.  Without the right groundwork, you’re setting yourself up for spotty insights, unreliable...

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How to Prevent Microsoft Copilot From Giving Bad Answers

Microsoft Copilot is a powerful tool, but like any AI, it can provide incorrect or misleading answers. To ensure you’re getting the most accurate responses, it’s essential to understand how to prompt Copilot properly in order to prevent bad outputs.  Let’s explore how Microsoft...

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LLM Evaluation Metrics for Reliable and Optimized AI Outputs

As the deployment of Large Language Models (LLMs) continues to expand across sectors such as healthcare, banking, education, and retail, the need to understand and effectively evaluate their capabilities grows with each new application. Solid LLM evaluation metrics for assessing output quality are...

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How to Form an AI Ethics Board for Responsible AI Development

Generative AI has presented businesses with unprecedented access to data and the tools to mine that data. It’s tempting to see all data as beneficial, but the older-than-AI rule, Garbage In, Garbage Out, still applies. To truly understand the effectiveness and safety of GenAI in your...

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Your Blueprint for AI Audits — Ensuring Ethical, Accurate, and Compliant AI

As companies work to ensure the accuracy, compliance, and ethical alignment of their AI systems, they are increasingly recognizing the importance of AI audits in their governance toolkits.  What Is an AI Audit? An AI audit is a comprehensive examination of an AI system that scrutinizes its...

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Inherently Interpretable ML: Tackling Untraceable Errors and Undetected Biases

Machine learning (ML) systems often operate behind complex algorithms, leading to untraceable errors, unjustified decisions, and undetected biases. In the face of these issues, there is a shift towards using interpretable models that ensure transparency and reliability. This shift is crucial for...

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How to Optimize a Machine Learning Pipeline for Faster Deployment

Machine learning pipelines automate and streamline the development, deployment, and maintenance of machine learning models. They ensure consistency, reduce manual effort, enhance scalability, and improve the reliability of your machine learning projects.  Ultimately, this automation...

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In-depth Guide to Machine Learning (ML) Model Deployment

Machine learning (ML) offers powerful tools for predictive analytics, automation, and decision-making. By analyzing vast amounts of data, ML models can uncover unique patterns and insights. This can drive efficiency, innovation, and competitive advantage for your organization. But, the true value...

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Precision-Driven Human Feedback Techniques for Optimal AI Performance

Real-world AI systems rely heavily on human interactions to refine their capabilities. Embedding human feedback ensures these tools evolve through experiential learning. Regular, informed user feedback allows AI systems to self-correct and align more closely with user expectations. However,...

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How Implementing Data Cleaning Can Boost AI Model Accuracy

The quality of your data can make or break your business decisions. Data cleaning, the process of detecting and correcting inaccuracies and inconsistencies in data, is essential for maintaining high-quality datasets. Clean data not only enhances the reliability of your analytics and business...

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Fairness Metrics in AI: Your Step-by-Step Guide to Equitable Systems

Fairness metrics are quantitative measures used to assess and mitigate bias in machine learning models. They help identify and quantify unfair treatment or discrimination against certain groups or individuals. As AI systems grow in influence, so does the risk of perpetuating or amplifying biases...

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