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LLM-based Prompt Generation from a Task Description
A method for generating initial prompts is to instruct a Large Language Model (LLM) by providing it with a human-written description of the task. While this approach is direct and straightforward, it is dependent on the availability of a human-provided task description.
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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
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Manual Creation of Initial Prompts
LLM-based Prompt Generation from Input-Output Examples
LLM-based Prompt Generation from a Task Description
Comparison of LLM-based Prompt Generation Methods for Initialization
A development team is tasked with finding the optimal prompt for a novel and complex text classification task. They plan to use an automated, multi-stage process that repeatedly evaluates, refines, and generates new prompts. Given that the team has very little prior experience with what constitutes a good prompt for this specific task, which of the following actions is the most critical first step to ensure their automated search process is effective?
Starting a Prompt Optimization Project
An automated, iterative process is often used to discover effective prompts. Arrange the following fundamental stages of this process into the correct chronological order.
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Example of an Instruction for LLM-based Prompt Generation
A team is tasked with creating a system to summarize legal documents. To begin, they write a detailed, two-page description of the desired summary format, key information to include, and the target audience. They then input this description into a large language model with the instruction: 'Based on the provided document, generate a set of effective prompts for a summarization task.' When critiquing this team's strategy for creating an initial set of prompts, what is the most critical factor that will determine the success of their approach?
Troubleshooting Initial Prompt Generation
Evaluating a Prompt Generation Strategy