Prompt Optimization Strategy Selection
A machine learning team is working to improve the performance of a large language model on a new task. They have a very limited computational budget. They are considering two different approaches to enhance their prompt. Based on the typical challenges of prompt engineering, which of the following two strategies is more likely to be a cost-effective and successful use of their limited resources? Justify your reasoning.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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When designing prompts for a large language model, why is the process of optimizing the instructions generally considered a more significant computational challenge than optimizing the demonstrations (examples)?
Prompt Optimization Strategy Selection
When automatically optimizing a prompt, the main challenge associated with the demonstrations (examples) is the difficulty of generating high-quality candidates, whereas the main challenge for the instructions is the difficulty of sampling the best ones from a large generated pool.