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Dive into the critical competencies of evaluating AI prompt performance in our intensive online module designed for advanced learners. This course will cover a range of essential topics including understanding evaluation metrics like accuracy, precision, recall, and F1 score; interpreting confusion matrices; analyzing ROC and AUC; assessing bias and fairness; ensuring transparency and explainability; upholding data privacy and security; utilizing performance benchmarks; designing real-world testing scenarios; handling anomalies and outliers; and conducting statistical significance testing. By the end of this module, you will be equipped with the practical skills and knowledge to critically assess and improve AI prompt performance in various applications.
Here is the course outline:
1. Introduction to Evaluating Prompt PerformanceThis foundational module introduces the core concepts and competencies needed to evaluate prompt performance, including understanding evaluation metrics, interpreting confusion matrices, and the basics of ROC and AUC analysis. The module will also touch upon the importance of bias and fairness assessment, transparency, and explainability in AI models, as well as considerations for data privacy and security. We will explore how to establish performance benchmarks and the significance of real-world testing scenarios. Additionally, we will discuss the handling of anomalies and outliers, and the role of statistical significance testing in performance evaluation. 4 sections
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