Constructing a Contextual Prompt for a Language Model
Based on the principle of providing cumulative context for multi-step problem-solving, construct the complete input prompt that should be given to the model to solve the second sub-problem: 'What is the capital of France?'.
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Example of Sequential Sub-Problem Solving: Environmental Study Duration
A large language model is tasked with solving a multi-step math problem. The overall problem is: 'A customer buys a laptop for $1200. The sales tax is 8%. What is the total cost?'. The model first solves a sub-problem: 'What is the sales tax amount for a $1200 laptop at an 8% tax rate?', and correctly answers '$96'. To solve the next step, which of the following inputs correctly provides the necessary context for the model to calculate the final total cost?
A language model is tasked with solving a multi-step problem. To solve the second sub-problem, it must be provided with the full context of the original problem and the previously solved sub-problem. Arrange the following components into the correct logical sequence to form a complete and effective input prompt for the model.
Constructing a Contextual Prompt for a Language Model