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🧙 PromptWizard
Task-Aware Prompt

Overview

- Large language models (LLMs) like GPT-4 have achieved remarkable performance across diverse tasks. At the core of this success is prompting—the process of providing input instructions to guide models toward desired outputs. Studies have shown that prompting significantly influences LLM performance, making prompt engineering—the design and refinement of prompts—critical for maximizing accuracy. However, crafting effective prompts remains a labor-intensive and domain-specific task, requiring human expertise and subjective judgment. As models evolve and tasks vary, the need to repeatedly design prompts raises an important question: + Large language models (LLMs) like GPT-4 have achieved remarkable performance across diverse tasks. At the core of this success is prompting—the process of providing input instructions to guide models toward desired outputs. Studies have shown that prompting significantly influences LLM performance, making prompt engineering—the design and refinement of prompts—critical for maximizing accuracy. However, crafting effective prompts remains a labor-intensive and domain-specific task, requiring human expertise and subjective judgment. As models evolve and tasks vary, the need to repeatedly design prompts raises an important question:
Can prompt engineering be automated to streamline this process and enhance scalability?
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Motivation

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Prompting is central to LLMs!

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  • Prompting: The process of providing input instructions to guide models towards desired output
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  • Prompt Engineering: The process of designing and refining of prompts​
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  • Crating effective prompts is a challenge as:​
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    1. The task is labor-intensive
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    2. Prompts need to be domain-specific to work effectively
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    3. Often it equires human expertise and is subjective​
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    4. Also as models and tasks evolve, there is a need for repeated design
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