This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype to Production: Evaluation Strategies for Agentic Applications.
Task-based evaluations, which measure an AI system’s performance in use-case-specific, real-world settings, are underadopted and understudied. There is still an outsized focus in AI literature on foundation model benchmarks. Benchmarks are essential for advancing research and comparing broad, general capabilities, but they rarely translate cleanly into task-specific performance.
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