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Metrics have always been the so-called magic beans for improving quality in testing and quality engineering. In her article, Lisa Crispin emphasizes the importance of measuring AI success and quality improvement. She shares her decades of experience in the field and highlights how continuous quality improvement is crucial for modern software teams. The discussion revolves around effective metrics that can drive quality initiatives, making the case for an analytical approach to testing. Crispin brings a fresh perspective on harnessing metrics to elevate both AI performance and overall product quality.
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