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Troubleshooting a Prompt Optimization Process
Analyze the team's methodology. Identify the key operation they are neglecting in their search for a better prompt and explain how systematically incorporating this operation would help them break out of their current cycle of poor results.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Example of a Prompt for LLM-based Prompt Expansion
Iterative LLM-Based Prompt Search
A team is using an automated process to discover a high-performing prompt for a text summarization task. They begin with an initial set of prompts:
C = {'Summarize the following text for me.', 'Give me the main points of this article.'}. They then apply a single 'expansion' operation, which uses a large language model to generate a new set of candidate prompts based on the ones inC. Which of the following best represents a plausible output set from this single expansion operation?The Role of Expansion in Prompt Diversity
Expansion Function in Search Algorithms
Troubleshooting a Prompt Optimization Process
Prompt Expansion via Edit Operations
Prompt Expansion via Feedback
Prompt Paraphrasing