Stop Trusting Skills You Haven't Measured
Blog post from Paper Compute Company
The text emphasizes the importance of evidence-based skill development and continuous evaluation to maintain effectiveness in AI-driven workflows. It argues that skills should emerge from successful, real-world sessions rather than memory, as this ensures they are grounded in proven practices. However, even skills derived from evidence must be regularly tested to confirm their continued relevance and accuracy, as they can become outdated due to context drift. Unlike static documents, skills are actively executed, meaning incorrect ones can lead to inefficiencies or errors. The piece highlights the necessity of evaluating skills through a process that compares agent performance with and without the skill, using real-world data to ensure that the skill genuinely enhances performance. This ensures that skill libraries remain useful, allowing teams to refine or retire skills as needed to prevent degradation of their AI systems' output quality.
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