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prompt-engineering-patterns

Design, optimize, and evaluate prompts for large language models.

2|Updated Jul 13, 2025
Search Tags:#prompt engineering#chain-of-thought#few-shot learning#LLM#template systems
One-click install
npx skills add https://github.com/krzemienski/shannon --skill prompt-engineering-patterns-krzemienski
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/krzemienski/shannon/tree/main/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/krzemienski/shannon --skill prompt-engineering-patterns-krzemienski

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill unit solves the problem of designing and optimizing prompts for Large Language Models (LLMs), enhancing their performance, reliability, and controllability in production environments.

Core Features & Use Cases

  • Advanced Prompt Engineering: Provides a comprehensive guide to advanced techniques like few-shot learning, chain-of-thought prompting, and template systems.
  • Optimization and Iteration: Offers frameworks for iterative prompt refinement and A/B testing to improve accuracy and consistency.
  • Structured Output: Ensures that LLM outputs are in machine-parseable formats, enabling integration with downstream systems.
  • System Prompt Design: Guides the creation of system prompts that set model behavior, define output formats, and establish safety guidelines.

Quick Start

Use the prompt-engineering-patterns skill to design a prompt for your specific use case. Start by reviewing the available patterns and select the one that best fits your needs. For example, if you're optimizing prompts for classification tasks, consider using the "Zero-shot" or "Few-shot" patterns.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
What is chain-of-thought prompting and when should I use it for LLM tasks?▼

Chain-of-thought prompting is an advanced technique that guides an LLM through intermediate reasoning steps. Use it to maximize model performance and reliability on complex tasks requiring structured logic before producing a final output.

How do I design system prompts that ensure structured, machine-parseable LLM outputs?▼

Design system prompts to explicitly set model behavior, define strict output formats, and establish safety guidelines. This ensures LLM responses are consistently formatted for seamless integration with downstream production systems.

What's the best way to optimize and iterate on prompts for reliable LLM performance?▼

Optimize prompts using iterative refinement frameworks and A/B testing patterns. Evaluate accuracy and consistency across prompt variations using established metrics to maximize reliability and controllability in production environments.

How do I apply few-shot learning patterns to improve LLM classification accuracy?▼

Apply few-shot learning patterns by providing targeted examples within the prompt to guide the LLM's classification behavior. This technique leverages template systems to improve accuracy and consistency for specific use cases.

Can I use these prompt engineering patterns for production environments without external dependencies?▼

Yes, these prompt engineering patterns operate independently without external dependencies. They provide self-contained frameworks for template systems, prompt iteration, and structured output generation suitable for production environments.

Why are my LLM outputs inconsistent and how do template systems help control them?▼

LLM outputs become inconsistent without structured guidance. Template systems control responses by standardizing inputs and defining strict output schemas, establishing safety guidelines and ensuring reliable, machine-parseable integration.

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