<ul data-eligibleForWebStory="true"><li>The playbook emphasizes the importance of proper evaluation before shipping AI products to avoid failures.</li><li>Real-life examples highlight the significance of choosing the right metrics for AI projects.</li><li>One case study involved detecting lung tumors in CT scans, emphasizing the need for accurate recall rates in model evaluation.</li><li>Another example focused on predicting vehicle trajectories on highways, showing the importance of testing models in real scenarios beyond just accuracy.</li><li>Success in AI development requires an evaluation-first mindset, treating assessment as a debugger for AI models and iterating to improve performance continuously.</li><li>Teams should prioritize thorough testing and iteration to ensure AI products can fail safely, recover gracefully, and progress over time.</li></ul>

Ship It, Don’t Shipwreck It: An Evaluation-First Playbook for AI Products

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