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Why Your AI Product Needs Evals with Hamel Husain and Swyx
High Agency: The Podcast for AI Builders
Error Analysis: The Path to Improved AI Functionality
Begin error analysis by closely examining your AI's data to identify and categorize various error types. Start with code-based assertions, ensuring that fundamental errors are caught, then proceed to analyze which parts of your product experience the most errors. Develop an understanding of the root causes, whether they're related to prompting, optimization, or other issues, and focus on the most accessible opportunities for improvement. This iterative process not only addresses errors but also enhances your evaluation system's effectiveness. Using concrete examples from a product like ReChat's AI, Lucy, highlights the importance of categorizing features and pinpointing specific errors, such as unintended UUID emissions or text/UI integration issues. By methodically examining data, you'll uncover numerous errors that can be turned into actionable tests, ultimately refining your AI's performance.
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