How does this skill improve LLM reliability?

It implements structured patterns like Chain-of-Thought reasoning and few-shot learning, which provide the model with better context and logical steps, significantly reducing hallucinations and inconsistent outputs.

What is the benefit of dynamic example selection?

Dynamic selection uses techniques like semantic similarity to choose the most relevant examples for a specific query, ensuring the LLM has the most pertinent context without wasting tokens on irrelevant demonstrations.

Can I use this for production API development?

Yes, it includes patterns for system prompt design, error recovery, and performance optimization specifically tailored for production environments where cost and latency are critical.

Does this skill help with token management?

Yes, it includes best practices for token efficiency, such as removing redundant instructions and consolidating prompts to minimize costs and improve response times.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: nguyendinhquocx

bynguyendinhquocx

0•

データサイエンスとML

Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.

The Prompt Engineering Patterns skill provides a comprehensive framework for designing and optimizing LLM interactions within the Claude Code environment. It equips developers with sophisticated techniques like few-shot learning for dynamic example selection, structured reasoning through Chain-of-Thought (CoT) patterns, and robust template systems for modular prompt composition. By utilizing these battle-tested patterns, developers can move beyond simple instructions to create production-grade AI systems that offer consistent outputs, handle edge cases gracefully, and optimize for both latency and token efficiency.

主な機能

01Performance optimization for token usage and response latency

02Few-shot learning with dynamic semantic similarity selection

030 GitHub stars

04Modular prompt template systems with variable interpolation

05Structured reasoning via Chain-of-Thought (CoT) and self-consistency

06Instruction hierarchy and progressive disclosure patterns

ユースケース

01Optimizing inconsistent AI outputs for specialized technical tasks

02Developing high-reliability production LLM applications

03Implementing complex reasoning workflows in RAG-based systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nguyendinhquocx/code-ai prompt-engineering-patterns

For use in Claude.ai and ChatGPT

Download Skill