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- Governance recommendations for AI workloads on Azure
Document identified risks and continuously update your AI governance policies to mitigate emerging issues Define baseline content filters for generative AI models Use Azure AI Content Safety to define a baseline content filter for your approved AI models This safety system runs both the prompt and completion for your model through a group of
- Maintaining High Quality: How to Assess AI-Generated Output
Another quality aspect is the AI model and operation stage of that model The model itself is controlled by the LLM provider, but the fine-tuning, thresholds like temperature (how confident does the model need to be to use the information in the AI response), and the prompt sent to the LLM can be controlled by your organization or by the researcher themselves if they are using an LLM in their
- Six Steps to Ensure AI Systems Share Accurate Information
The first step to ensure AI systems share accurate information is to define the criteria that measure the quality of the information Depending on the context and the purpose of the information
- AI Quality: 4 Dimensions and Processes for Managing . . . - Kolena
Organizations developing AI systems must evaluate AI quality to ensure their products are useful, effective, and safe for use However, the concept of AI quality is not only significant for AI developers It is crucial for anyone who uses AI systems AI quality information empowers users to make informed decisions and ensure that AI systems are
- Data Quality in AI: Challenges, Importance Best Practices
What is the importance of data quality in AI? Data quality is essential for artificial intelligence, as it directly influences the performance, accuracy, and reliability of AI models High-quality data allows models to make better predictions and yield more reliable outcomes The impact of poor data quality in AI is illustrated in Figure 1
- Prompt Engineering Checklist: Optimize Your AI Outputs
By refining your prompts and focusing on clarity, you can greatly enhance the quality of the AI’s responses [2] [4] Adding examples and relevant context to your instructions can further improve the AI’s ability to deliver accurate and useful outputs 2 Add Examples and Context Adding examples and context helps the AI better understand
- AI Data Governance Best Practices for Security and Quality . . .
Poor-quality data leads to poor AI decisions Inconsistent, incomplete, or outdated datasets can skew predictions and recommendations Implement data validation, cleansing, and standardization processes so that your AI models use high-quality, relevant data Regular audits can prevent your systems from making decisions based on bad inputs 4
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