How does this skill help with few-shot learning?

It provides strategies for dynamic example selection using semantic similarity and diversity sampling to provide the model with the best context for a task.

Can I use these patterns for production applications?

Yes, this skill is specifically designed for building production-ready prompts that include error recovery, structure validation, and performance monitoring.

What are the benefits of using prompt engineering patterns?

These patterns significantly improve the reliability, accuracy, and consistency of LLM responses while reducing costs through better token management.

Will this help reduce my API costs?

Yes, by following the performance optimization patterns, you can learn to remove redundant instructions and consolidate prompts to lower token usage.

Does it include chain-of-thought techniques?

Absolutely. It covers zero-shot CoT, few-shot reasoning traces, and self-consistency techniques to solve complex logical problems.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

データサイエンスとML

Implement advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.

概要

The Prompt Engineering Patterns skill provides developers with a comprehensive framework for optimizing Large Language Model (LLM) interactions. By leveraging sophisticated techniques like few-shot learning, chain-of-thought reasoning, and modular template systems, this skill enables the creation of highly reliable and predictable AI-driven applications. It focuses on practical production needs, including reducing token overhead, managing system prompts, and implementing iterative refinement workflows to ensure high-quality outputs across complex use cases.

主な機能

Advanced few-shot learning strategies for dynamic and semantic example selection.
0 GitHub stars
Chain-of-thought patterns to elicit structured, step-by-step reasoning from models.
System prompt design guidelines for establishing behavior, constraints, and safety.
Modular prompt template systems with variable interpolation and conditional logic.
Performance optimization workflows to reduce latency and minimize token consumption.

ユースケース

Refining inconsistent LLM outputs through systematic A/B testing and reasoning elicitation.
Integrating RAG systems with optimized context-handling and validation patterns.
Designing robust prompts for production-grade AI features and specialized assistants.

概要

主な機能

Advanced few-shot learning strategies for dynamic and semantic example selection.
0 GitHub stars
Chain-of-thought patterns to elicit structured, step-by-step reasoning from models.
System prompt design guidelines for establishing behavior, constraints, and safety.
Modular prompt template systems with variable interpolation and conditional logic.
Performance optimization workflows to reduce latency and minimize token consumption.

ユースケース

Refining inconsistent LLM outputs through systematic A/B testing and reasoning elicitation.
Integrating RAG systems with optimized context-handling and validation patterns.
Designing robust prompts for production-grade AI features and specialized assistants.