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prompt-engineer

Designs, optimizes and evaluates prompts for LLMs using techniques like chain-of-thought and few-shot learning.

10|3|Updated Apr 3, 2012
Search Tags:#prompt engineering#chain-of-thought#few-shot learning#llm optimization#structured outputs#prompt evaluation
One-click install
npx skills add https://github.com/liuerfire/dotfiles --skill prompt-engineer-liuerfire
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/liuerfire/dotfiles/tree/main/agentic/skills/prompt-engineer
Command: npx skills add https://github.com/liuerfire/dotfiles --skill prompt-engineer-liuerfire

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of designing, refining, and evaluating prompts to maximize the performance and reliability of Large Language Models (LLMs).

Core Features & Use Cases

  • Prompt Design: Craft effective prompts for various LLM tasks, from simple Q&A to complex reasoning.
  • Optimization: Systematically improve prompt accuracy, efficiency, and consistency.
  • Evaluation Frameworks: Build and utilize robust methods for testing prompt performance.
  • Use Case: You need to create a prompt that reliably extracts specific information from customer feedback emails. This Skill helps you design the initial prompt, test it against various feedback styles, and optimize it for accuracy and conciseness.

Quick Start

Use the prompt-engineer skill to design a prompt for summarizing technical documents.

Frequently Asked Questions about prompt-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design LLM prompts for complex reasoning tasks?▼

To design LLM prompts for complex reasoning, apply techniques like chain-of-thought and few-shot learning. This Skill helps craft prompts that guide the model step-by-step, improving accuracy and reliability for intricate reasoning tasks.

What is the best way to evaluate prompt performance for large language models?▼

Evaluating prompt performance requires building robust evaluation frameworks to test accuracy and consistency. This Skill provides methodologies to systematically measure and refine LLM outputs against various test cases and expected results.

How do I optimize LLM prompts to generate structured outputs?▼

Optimizing LLM prompts for structured outputs involves iterative refinement of your instructions to enforce formatting rules. This Skill helps systematically improve prompt design to consistently yield structured and parsable model responses.

Why does my LLM prompt produce inconsistent results across different inputs?▼

Inconsistent LLM prompt results often stem from a lack of systematic optimization and clear instructions. This Skill addresses this by providing iterative refinement strategies and evaluation frameworks to enhance prompt reliability across diverse inputs.

Can I use few-shot learning to improve LLM accuracy for specific extraction tasks?▼

Yes, few-shot learning improves LLM accuracy for specific extraction tasks by providing examples within the prompt. This Skill helps you design and test few-shot prompts to reliably extract targeted information from varied text formats.

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