Analyzing Prompt Complexity
Consider the two prompts below designed for a large language model:
Prompt A:
Classify the sentiment of the following movie review as 'positive', 'negative', or 'neutral'. Review: [Movie Review Text]
Prompt B: `You are an AI assistant for a new tech startup. Your task is to categorize customer feedback into one of three custom labels: 'Feature Request', 'Bug Report', or 'Usability Issue'.
- 'Feature Request' suggests a new capability.
- 'Bug Report' describes something that is broken or not working as intended.
- 'Usability Issue' describes a part of the product that is confusing or difficult to use. Feedback: [Customer Feedback Text]`
Explain why Prompt B must include significantly more detail than Prompt A to achieve a reliable and accurate classification from the model.
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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
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A developer needs to use a large language model for two distinct purposes. The first task is to summarize long news articles into a single paragraph. The second task is to categorize employee performance notes into a new, company-specific framework with three tiers ('Exceeds Expectations', 'Meets Expectations', 'Needs Development'), based on a unique set of internal performance indicators. Which of the following prompting strategies would be most effective?
Analyzing Prompt Complexity