A Framework for Systematic Prompt Optimization as Model Selection
By
neehao
Crackles when you bite it. Shows the baker did the work.
Summary
This article presents a framework for prompt optimization in AI/ML systems, treating it as a model selection problem. It emphasizes defining clear success metrics and evaluation criteria before data collection, including primary business-value metrics (accuracy, F1, BLEU/ROUGE) and auxiliary constraints. The approach focuses on systematic optimization rather than random prompt tweaking.
Key quotes
· 4 pulledBefore collecting any data, establish what success looks like for your specific use case
Choose a primary metric that directly reflects business value—accuracy for classification, F1 for imbalanced datasets, BLEU/ROUGE for generation tasks
This primary metric drives optimization decisions
Define auxiliary constraints that you won't compromise on
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