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prompt-engineering-patterns

Design and optimize production prompts and templates for large language models.

5|Updated Aug 23, 2025
Search Tags:#prompt engineering#prompt optimization#ai development#chain-of-thought#ai#llm#few-shot learning
One-click install
npx skills add https://github.com/camoneart/claude-code --skill prompt-engineering-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/camoneart/claude-code/tree/main/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/camoneart/claude-code --skill prompt-engineering-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sentence-transformers, numpy, scikit-learn, openai, scipy, and includes assets (resource) and references (resource) and scripts (resource) components.

What problem does it solve?

Maximizing the performance, reliability, and controllability of Large Language Models (LLMs) in production requires sophisticated prompt engineering. This Skill provides advanced techniques to design, optimize, and manage prompts for consistent and high-quality outputs.

Core Features & Use Cases

  • Few-Shot Learning: Guides on example selection strategies (semantic similarity, diversity) and context window management.
  • Chain-of-Thought Prompting: Covers zero-shot, few-shot, self-consistency, and advanced reasoning patterns like Tree-of-Thought.
  • Prompt Optimization: Explains iterative refinement, A/B testing, token reduction, and latency optimization.
  • Use Case: When developing a critical LLM application, this Skill helps you design prompts that reduce hallucinations, improve accuracy, and ensure the model follows specific output formats, leading to more reliable AI systems.

Quick Start

Example: Structured prompt template with few-shot learning

This demonstrates how to define a template and select examples dynamically.

from prompt_optimizer import PromptTemplate, FewShotSelector

template = PromptTemplate( system="You are an expert SQL developer.", instruction="Convert natural language to SQL: {query}", few_shot_examples=True, output_format="SQL code block" )

selector = FewShotSelector(examples_db="sql_examples.jsonl")

prompt = template.render(query="Find users", examples=selector.select(...))

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I reduce hallucinations and improve accuracy in LLM outputs?▼

Prompt engineering techniques like chain-of-thought reasoning, few-shot learning with semantic similarity, and structured output formats significantly reduce hallucinations and improve LLM accuracy. This Skill provides advanced patterns for designing production prompts that enforce consistent, reliable model behavior.

What's the best way to optimize prompts for production LLM applications?▼

Optimize prompts through iterative refinement, A/B testing, token reduction, and latency tuning while applying few-shot learning and chain-of-thought patterns. This Skill covers modular template composition with variable interpolation and dynamic example retrieval for consistent performance.

How do I implement few-shot learning with effective example selection?▼

Few-shot learning uses semantic similarity and diversity-based strategies to select examples that fit your context window. This Skill explains example selection methods and provides template systems for dynamically retrieving and composing examples into prompts.

Can I use chain-of-thought prompting to improve reasoning in my LLM?▼

Yes. Chain-of-thought prompting—including zero-shot, few-shot, self-consistency, and Tree-of-Thought patterns—guides LLMs through step-by-step reasoning. This Skill provides advanced prompting patterns optimized for production reasoning workflows and RAG-based systems.

How do I build reusable prompt templates with dynamic content and conditional sections?▼

Build modular prompt templates using variable interpolation, conditional sections, and verification steps. This Skill demonstrates template composition, example retrieval from databases, and output format specification for production-grade prompt management.

What dependencies or environment setup do I need for prompt engineering?▼

Prompt engineering workflows use sentence-transformers for semantic similarity, scikit-learn for example selection, and OpenAI or similar LLM APIs. This Skill integrates numpy and scipy for numerical operations and provides scripts for template rendering and example management.

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