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prompt-engineering-patterns

Design structured prompt templates and reusable patterns for LLM performance.

2|Updated May 20, 2026
Search Tags:#prompt engineering#prompt optimization#chain-of-thought#few-shot learning#system prompts#prompt templates#RAG integration
One-click install
npx skills add https://github.com/Dianshu-Liao/SkilLGuard --skill prompt-engineering-patterns-dianshu-liao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/Dianshu-Liao/SkilLGuard/tree/main/data/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/Dianshu-Liao/SkilLGuard --skill prompt-engineering-patterns-dianshu-liao

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you design and refine LLM prompts so outputs are more accurate, consistent, controllable, and robust under real-world conditions.

Core Features & Use Cases

  • Few-shot learning workflows: select and structure examples to improve task accuracy and formatting.
  • Reasoning and verification patterns: apply chain-of-thought, self-consistency, and explicit error recovery steps.
  • Prompt optimization and template systems: iterate on prompt variants while tracking performance, token usage, and latency.
  • System prompt design for behavior control: set roles, constraints, and output structures for dependable assistant behavior.
  • Integration-ready patterns: combine prompts with RAG context and validation steps for safer grounded answers.

Quick Start

Use this skill to improve a production prompt by asking an AI to rewrite your instruction set into a structured template using few-shot examples and an explicit verification checklist.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I design prompt templates for reliable LLM production behavior?▼

Design prompt templates by specifying system roles, constraints, output structures, and explicit verification steps to control LLM behavior reliably in production environments.

What is the best way to apply few-shot learning for prompt optimization?▼

Few-shot learning improves prompt optimization by selecting and structuring examples within the prompt to enhance task accuracy and enforce consistent output formatting.

How does chain-of-thought prompting improve structured reasoning?▼

Chain-of-thought prompting improves structured reasoning by guiding the LLM through explicit intermediate steps, applying self-consistency, and defining error recovery workflows.

Can I integrate prompt patterns with RAG context grounding?▼

Yes, integration-ready prompt patterns combine instructions with RAG context and validation steps to deliver safer, grounded answers for production deployment.

How do I track token usage and latency when iterating on prompt variants?▼

Track token usage and latency during prompt optimization by iterating on template variants while monitoring performance metrics to ensure production readiness.

Do I need numpy to build prompt templates and reasoning workflows?▼

Yes, building structured prompt templates, reasoning workflows, and few-shot selection logic requires the numpy dependency to process underlying data structures.

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