What are prompt engineering patterns?

Prompt engineering patterns are standardized, reusable techniques like few-shot learning, chain-of-thought, and instruction hierarchies used to improve the consistency and quality of LLM responses.

How does this skill improve AI reliability?

By implementing structured reasoning traces, strategic examples, and error-recovery instructions, the skill reduces model hallucinations and ensures the LLM follows production constraints.

Can I use these patterns with RAG systems?

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

Does this help reduce API costs?

Absolutely. It includes performance optimization strategies focused on token efficiency, helping you remove redundant instructions while maintaining output quality.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: Krosebrook

byKrosebrook

•

データサイエンスとML

Optimizes LLM performance and reliability through advanced prompt engineering techniques and structured reasoning patterns.

概要

The Prompt Engineering Patterns skill provides a comprehensive framework for maximizing Large Language Model (LLM) performance, reliability, and controllability in production environments. It equips developers with standardized implementation patterns for few-shot learning, chain-of-thought reasoning, and modular prompt templates, enabling the creation of sophisticated AI applications with consistent, high-quality outputs. By incorporating systematic optimization workflows, instruction hierarchies, and error-recovery strategies, this skill helps developers reduce token usage, improve latency, and ensure that AI responses strictly adhere to specific domain constraints and safety guidelines.

主な機能

System prompt design for role-based behavior, constraints, and safety policies.
Few-shot learning with dynamic example selection and semantic similarity.
Chain-of-thought and structured reasoning patterns to elicit complex logic.
Modular prompt template systems with variable interpolation and conditional logic.
Performance optimization workflows to reduce token usage and improve latency.
1 GitHub stars

ユースケース

Debugging and refining prompts that produce inconsistent or hallucinated outputs.
Designing complex, multi-step prompts for production-grade LLM applications.
Scaling AI development through reusable prompt templates and automated versioning.

概要

主な機能

System prompt design for role-based behavior, constraints, and safety policies.
Few-shot learning with dynamic example selection and semantic similarity.
Chain-of-thought and structured reasoning patterns to elicit complex logic.
Modular prompt template systems with variable interpolation and conditional logic.
Performance optimization workflows to reduce token usage and improve latency.
1 GitHub stars

ユースケース

Debugging and refining prompts that produce inconsistent or hallucinated outputs.
Designing complex, multi-step prompts for production-grade LLM applications.
Scaling AI development through reusable prompt templates and automated versioning.