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The process of designing discrete prompts is analogous to designing discrete machine learning model architectures. Consequently, automated prompt design shares similarities with Neural Architecture Search (NAS), a field dedicated to automatically discovering the most effective neural network structures by exploring a vast space of possibilities.

Comparison between Automated Prompt Design and Neural Architecture Search

What fundamental principle establishes a strong analogy between the automated design of discrete prompts and the search for optimal neural network architectures?

Drawing an analogy to other areas of machine learning, which established field dedicated to automatically discovering optimal model *structures* would likely offer the most relevant search algorithms and optimization strategies for the team's problem? Justify your answer by explaining the fundamental similarity between the two challenges.

Applying Search Strategies Across Domains

The automated discovery of optimal wording for instructions given to a large model is often compared to the automated discovery of optimal arrangements of components within a neural network. Analyze this comparison by explaining two fundamental aspects that these two distinct search problems have in common.

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