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prompt-engineering-patterns

Design, debug, and optimize prompts for LLM applications with Python.

Updated Jul 7, 2026
Search Tags:#prompt engineering#prompt optimization#chain-of-thought#few-shot learning#structured outputs
One-click install
npx skills add https://github.com/voidrot/agents --skill prompt-engineering-patterns-voidrot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/voidrot/agents/tree/main/skills/meta/prompt-engineering-patterns
Command: npx skills add https://github.com/voidrot/agents --skill prompt-engineering-patterns-voidrot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain_voyageai, anthropic, pydantic, numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps users design, debug, and optimize prompts, prompt templates, few-shot examples, structured outputs, and advanced prompting workflows for LLM applications.

Core Features & Use Cases

  • Prompt Engineering: Provides guidelines and best practices for designing effective prompts.
  • Few-Shot Learning: Offers strategies for selecting and constructing examples to enhance model performance.
  • Chain-of-Thought (CoT): Delivers techniques for structuring reasoning and verification steps in prompts.
  • Structured Outputs: Implements JSON mode for reliable parsing and structured data handling.
  • Prompt Optimization: Suggests iterative refinement, A/B testing, and performance tracking.
  • Template Systems: Facilitates variable interpolation, conditional prompts, and modular components.
  • System Prompt Design: Sets role, expertise, constraints, and output expectations for LLM behavior.
  • Use Case: Consider a scenario where a user needs to create a sophisticated prompt for a language model to perform complex reasoning tasks. This Skill can guide the user in structuring the prompt, selecting relevant examples, and optimizing its performance.

Quick Start

Run the 'optimize-prompt.py' script to automatically test and optimize your prompts.

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 prompts for LLM applications using chain-of-thought and few-shot learning?▼

Structured outputs implement JSON mode for reliable parsing in LLM applications. By enforcing structured data handling, prompts yield consistent JSON responses, enabling robust extraction and integration with downstream systems.

How do I run A/B testing and performance tracking for prompt optimization?▼

A/B testing for prompt optimization requires Python with LangChain, Anthropic, Pydantic, NumPy, and SciPy dependencies. These libraries enable automated performance tracking, statistical evaluation, and structured output validation during prompt testing.

What are the best practices for system prompt design and structured outputs?▼

System prompt design best practices involve setting role, expertise, constraints, and output expectations for LLM behavior. Structured outputs implement JSON mode for reliable parsing, while template systems facilitate variable interpolation and modular components.

Why does my LLM prompt fail at complex reasoning tasks and text classification?▼

LLM prompts fail at complex reasoning and text classification without structured reasoning steps, relevant few-shot examples, or enforced structured JSON outputs. Iterative refinement and A/B testing are required to debug and resolve these performance gaps.

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