W publikacji „Machine Learning Mastery” stwierdzono, że agenci AI wykorzystywani w środowisku produkcyjnym często marnują tokeny z powodu powszechnie stosowanych wzorców projektowych, a poprawki architektoniczne mają na celu zwiększenie wydajności.
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