What are prompt engineering patterns?

Prompt engineering patterns are standardized, reusable techniques like chain-of-thought and few-shot learning used to improve the accuracy, reasoning, and formatting of LLM outputs.

How does this skill help with production AI applications?

It provides structured frameworks for template interpolation, error recovery, and iterative refinement, ensuring that LLM responses remain consistent and reliable at scale.

Can I use these patterns for RAG systems?

Yes, the skill includes specific integration patterns for combining retrieved context with instruction hierarchy to ensure the model stays grounded in provided data.

How do these patterns optimize token usage?

The skill focuses on token-efficient instruction design, consolidate redundant phrases, and strategic placement of stable content within system prompts to minimize costs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

데이터 과학 및 ML

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

소개

Prompt Engineering Patterns provides a robust framework for mastering sophisticated LLM interaction strategies directly within the Claude Code environment. It equips developers with standardized implementation patterns for advanced techniques such as few-shot learning, chain-of-thought reasoning, and modular prompt templating. By applying these battle-tested patterns, developers can significantly improve the consistency of AI outputs, reduce token overhead, and implement complex reasoning workflows, making it an essential tool for anyone building production-grade AI features or specialized autonomous agents.

주요 기능

Structured Chain-of-Thought patterns for complex reasoning elicitation
Modular prompt template system with variable interpolation and logic
Standardized system prompt design for specialized AI behavior and safety
0 GitHub stars
Advanced Few-Shot Learning with dynamic example selection strategies
Optimization workflows to improve output accuracy and reduce latency

사용 사례

Implementing complex multi-step reasoning and self-verification logic
Building production-grade LLM applications that require high reliability
Optimizing prompt performance and reducing token costs for scale

소개

주요 기능

Structured Chain-of-Thought patterns for complex reasoning elicitation
Modular prompt template system with variable interpolation and logic
Standardized system prompt design for specialized AI behavior and safety
0 GitHub stars
Advanced Few-Shot Learning with dynamic example selection strategies
Optimization workflows to improve output accuracy and reduce latency

사용 사례

Implementing complex multi-step reasoning and self-verification logic
Building production-grade LLM applications that require high reliability
Optimizing prompt performance and reducing token costs for scale