Does it support complex logical reasoning?

Absolutely. It implements Chain-of-Thought and multi-step verification techniques to help the model break down complex tasks into manageable, logical steps.

Can I use these patterns with RAG systems?

Yes, the skill includes specific integration patterns for combining retrieved context with optimized prompts to improve the accuracy of grounded AI responses.

What are prompt engineering patterns?

Prompt engineering patterns are reusable techniques and structured approaches, such as Few-Shot learning or Chain-of-Thought, designed to improve the quality, logic, and reliability of LLM outputs.

How does this skill help with production LLM applications?

It provides standardized frameworks for testing, optimizing, and versioning prompts, ensuring that your AI application remains consistent and performant under various real-world inputs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: amurata

byamurata

•

Data Science & ML

Optimizes LLM performance and reliability through advanced prompt engineering techniques like few-shot learning and chain-of-thought reasoning.

Prompt Engineering Patterns provides a comprehensive framework for mastering LLM performance, reliability, and control in production environments. It empowers developers to implement sophisticated patterns such as dynamic few-shot learning, chain-of-thought reasoning, and modular template systems to ensure consistent and high-quality outputs. This skill is particularly valuable when building complex AI assistants, debugging inconsistent model behaviors, or optimizing token usage without sacrificing accuracy in large-scale applications.

Key Features

01Dynamic few-shot learning with semantic similarity selection

02Structured reasoning patterns including Chain-of-Thought and Self-consistency

03Modular prompt template system with variable interpolation

04Iterative prompt optimization and A/B testing workflows

05Advanced system prompt design for behavioral constraints and safety

063 GitHub stars

Use Cases

01Designing robust prompts for production-grade LLM applications

02Implementing specialized AI assistants with specific role-based behaviors

03Debugging and refining prompts that produce inconsistent or hallucinated outputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add amurata/cc-tools prompt-engineering-patterns

For use in Claude.ai and ChatGPT

Download Skill