Fdaytalk Homework Help: Questions and Answers: A user asks a Large Language Model to write an introductory message for a marketing campaign. After reviewing the initial output, the user enters several more prompts asking the model to make further revisions to the same text.
Which technique is described in this example?
a) prompt tokening
b) role-based prompting
c) shot-based prompting
d) prompt chaining
e) I don’ know this yet
Answer:
First, let’s understand what the question is asking: It’s a scenario where a user initially requests an introductory marketing message from a Large Language Model (LLM). After receiving the initial output, the user then provides additional prompts to revise and improve the same text.
Now, we need to find out which technique is described in this question.
Given Options: Step by Step Analysis
a) Prompt Tokening
- Prompt tokening is a technique where the user splits a prompt into smaller tokens to guide the language model’s output. However, in the example given, the user iteratively refines the output by providing additional prompts rather than tokenizing a single prompt
b) Role-Based Prompting
- This technique involves defining roles within the prompts to guide the model’s response. For example, asking the model to act as a specific character or expert. The scenario does not describe role-based prompting.
c) Shot-Based Prompting
- Shot-based prompting includes techniques like zero-shot, one-shot, or few-shot learning. These involve giving the model one or a few examples to guide its response. The scenario describes ongoing revisions rather than providing examples, so this does not fit.
d) Prompt Chaining
- Prompt chaining involves a sequence of prompts where each new prompt builds upon the response of the previous one. This fits the scenario where the user provides initial input and then continues to refine the response through additional prompts.
e) I Don’t Know This Yet
- We’ll consider this if none of the other options seem correct.
Conclusion
Based on the above analysis, the correct answer is:
d) prompt chaining
The scenario describes a process where the user iteratively refines the text generated by the model through a series of prompts. This matches the technique of prompt chaining, where each prompt builds upon the previous responses to refine the output.
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