How does this skill help with token optimization?

It includes strategies for removing redundancy, using instruction hierarchies, and consolidating prompts, helping you maintain high-quality outputs while minimizing context window usage and costs.

Can I use these patterns for different LLM providers?

Yes, these prompt engineering patterns are foundational techniques that apply to most major large language models, including Claude, GPT-4, and Llama-based models.

How does Chain-of-Thought (CoT) prompting improve outputs?

CoT encourages the model to generate intermediate reasoning steps, helping it solve complex logic problems and reducing hallucinations by breaking down tasks into manageable parts.

What are the benefits of using few-shot learning in prompts?

Few-shot learning provides the LLM with specific examples of the desired task, which significantly improves accuracy and adherence to specific formatting or stylistic requirements in production environments.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

数据科学与机器学习

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

关于

This skill empowers developers to build production-grade AI applications by implementing sophisticated prompt engineering strategies. It provides a structured framework for designing complex prompts, managing dynamic examples, and enforcing consistent model behavior. By utilizing battle-tested patterns such as progressive disclosure, instruction hierarchy, and self-verification, users can significantly improve the accuracy, controllability, and efficiency of LLM-generated outputs across diverse use cases.

主要功能

Structured Chain-of-Thought (CoT) reasoning for complex logical tasks.
Advanced Few-Shot learning with dynamic example selection and semantic similarity.
System prompt design for establishing model expertise, constraints, and safety guidelines.
Reusable prompt template systems with variable interpolation and role-based composition.
Performance optimization workflows to reduce token usage and minimize latency.
0 GitHub stars

使用场景

Designing complex natural language to code generation systems.
Building specialized AI assistants with specific domain expertise and safety guardrails.
Improving consistency in automated summarization and data extraction tasks.

关于

主要功能

Structured Chain-of-Thought (CoT) reasoning for complex logical tasks.
Advanced Few-Shot learning with dynamic example selection and semantic similarity.
System prompt design for establishing model expertise, constraints, and safety guidelines.
Reusable prompt template systems with variable interpolation and role-based composition.
Performance optimization workflows to reduce token usage and minimize latency.
0 GitHub stars

使用场景

Designing complex natural language to code generation systems.
Building specialized AI assistants with specific domain expertise and safety guardrails.
Improving consistency in automated summarization and data extraction tasks.