prompt-engineering-patterns

Design, optimize, and manage prompts for Large Language Models in production.

Updated Jan 13, 2026

Search Tags:#prompt engineering #optimization #chain-of-thought #llm #few-shot learning #prompt templates

Ready to run

npx skills add https://github.com/shinnytech/caiwenqiang-member-rank --skill prompt-engineering-patterns-shinnytech

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/shinnytech/caiwenqiang-member-rank/tree/main/.cursor/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/shinnytech/caiwenqiang-member-rank --skill prompt-engineering-patterns-shinnytech

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps you create, optimize, and manage prompts for Large Language Models (LLMs) to achieve better performance, reliability, and control in production environments.

Core Features & Use Cases

Advanced Prompting Techniques: Learn and apply methods like Few-Shot Learning, Chain-of-Thought, and Tree-of-Thought.
Prompt Optimization: Systematically improve prompt accuracy, consistency, and efficiency.
Template Systems: Design reusable and dynamic prompt templates.
Use Case: You need to build a chatbot that can accurately answer customer questions based on a knowledge base. This Skill provides the techniques to design prompts that ensure the chatbot retrieves relevant information and formulates clear, concise answers.

Quick Start

Use the prompt-engineering-patterns skill to generate a prompt for summarizing a given article.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema

How do I optimize LLM prompts for better reliability in production?▼

To optimize LLM prompts for production reliability, apply advanced prompt engineering patterns like few-shot learning, chain-of-thought reasoning, and systematic template design to enhance accuracy, consistency, and overall model control.

What is the best way to design dynamic prompt templates for Large Language Models?▼

The best way to design dynamic prompt templates for Large Language Models is to use structured templating systems that allow you to build reusable, parameterized prompts, ensuring consistent system prompt design and manageable prompt optimization.

How does chain-of-thought prompting improve LLM performance?▼

Chain-of-thought prompting improves LLM performance by guiding the model through intermediate reasoning steps before generating a final answer, which significantly enhances the accuracy and reliability of complex tasks in production environments.

When do I need few-shot learning in my prompt engineering workflow?▼

You need few-shot learning in your prompt engineering workflow when you must ensure the LLM performs specific tasks accurately without fine-tuning, by providing examples within the prompt to establish clear patterns and expected output formats.

Can I use prompt engineering patterns to build a customer service chatbot?▼

Yes, you can use prompt engineering patterns to build a customer service chatbot by designing system prompts that ensure the LLM accurately retrieves relevant information from a knowledge base and formulates clear, concise answers.