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prompt-engineering-patterns

Implement few-shot learning and chain-of-thought patterns for structured LLM outputs.

4|1|Updated Mar 14, 2026
Search Tags:#prompt engineering#chain-of-thought#few-shot learning#structured output#llm optimization#prompt patterns
One-click install
npx skills add https://github.com/GPTtang/skill-atlas --skill prompt-engineering-patterns-gpttang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/GPTtang/skill-atlas/tree/main/skills/ai-agent/prompt-engineering-patterns
Command: npx skills add https://github.com/GPTtang/skill-atlas --skill prompt-engineering-patterns-gpttang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you overcome the challenges of inconsistent, unreliable, or suboptimal outputs from Large Language Models by providing advanced techniques to craft highly effective prompts.

Core Features & Use Cases

  • Enhance LLM Performance: Improve accuracy, reliability, and controllability of LLM responses.
  • Structured Reasoning: Implement patterns like Chain-of-Thought and Tree-of-Thought for complex problem-solving.
  • Reliable Outputs: Utilize structured output formats (like JSON) for predictable and parseable results.
  • Use Case: When building an AI assistant that needs to reliably extract specific information from user queries in a structured format, or when developing a system that requires complex multi-step reasoning.

Quick Start

Use this skill to learn how to implement few-shot learning with dynamic example selection.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I improve LLM output reliability for production environments?▼

Improve LLM output reliability by applying advanced prompt engineering patterns like structured JSON outputs and few-shot learning to ensure predictable, parseable results in production systems.

What is chain-of-thought prompt engineering and when should I use it?▼

Chain-of-thought prompt engineering is a structured reasoning pattern that guides LLMs through multi-step problem solving, used when developing systems that require complex logical deduction and accurate intermediate steps.

How do I get structured JSON output from LLM responses?▼

Get structured JSON output from LLMs by designing production prompt templates that enforce structured output formats, ensuring responses are predictable and directly parseable by downstream applications.

What are the best prompt patterns for LLM optimization?▼

Best prompt patterns for LLM optimization include few-shot learning, chain-of-thought reasoning, and system prompt design, which collectively enhance model accuracy, controllability, and response consistency.

Why does my LLM output inconsistent results from the same prompt?▼

LLM output becomes inconsistent without production-grade prompt templates, but implementing structured output constraints and few-shot learning examples significantly enhances response reliability and controllability.

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