What are prompt engineering patterns?

Prompt engineering patterns are reusable strategies and structures, such as few-shot examples or instruction hierarchies, used to improve the performance, reliability, and accuracy of Large Language Models.

Does it support few-shot learning?

Absolutely. It provides strategies for semantic example selection and dynamic retrieval to provide the model with the most relevant demonstrations for a specific task.

Is this skill suitable for production LLM apps?

Yes, it focuses on 'production-grade' patterns, including error recovery, fallback instructions, and performance monitoring metrics essential for enterprise AI applications.

How does this skill help with token costs?

It includes optimization techniques to remove redundant instructions and consolidate prompts, reducing overall token usage and associated API costs without sacrificing output quality.

Can I use this for complex reasoning?

Yes, it implements Chain-of-Thought and Tree-of-Thought patterns specifically designed to elicit step-by-step reasoning and logical validation from AI models.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

0•

Ciencia de Datos y ML

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

This skill empowers developers to master sophisticated prompt engineering patterns for Claude and other LLMs, moving beyond simple instructions to production-grade implementations. It provides structured frameworks for few-shot learning, chain-of-thought reasoning, and dynamic template interpolation, ensuring consistent and high-quality outputs. By integrating specialized system prompts and error-recovery logic, it helps developers build robust AI-driven features that handle edge cases gracefully while optimizing for both token efficiency and response latency.

Características Principales

01Structured Chain-of-Thought (CoT) reasoning elicitation

02Dynamic few-shot learning with semantic example selection

03Modular prompt template systems with variable interpolation

040 GitHub stars

05Automated prompt optimization and A/B testing workflows

06System prompt design for specialized AI assistant roles

Casos de Uso

01Optimizing inconsistent LLM outputs for complex reasoning tasks

02Reducing token usage and latency in production AI applications

03Implementing reliable structured data extraction and SQL generation

Características Principales

01Structured Chain-of-Thought (CoT) reasoning elicitation

02Dynamic few-shot learning with semantic example selection

03Modular prompt template systems with variable interpolation

040 GitHub stars

05Automated prompt optimization and A/B testing workflows

06System prompt design for specialized AI assistant roles

Casos de Uso

01Optimizing inconsistent LLM outputs for complex reasoning tasks

02Reducing token usage and latency in production AI applications

03Implementing reliable structured data extraction and SQL generation