What are prompt engineering patterns?

They are standardized, reusable techniques used to improve the accuracy, reliability, and formatting of Large Language Model responses.

How does this skill improve production reliability?

It provides patterns for structured JSON outputs, Pydantic validation, and error recovery, ensuring that the AI integrates safely with your backend systems.

Does it help with token optimization?

Yes, it includes guidelines and patterns for progressive disclosure and token-efficient prompt design to reduce costs while maintaining quality.

Can I use this for complex logical tasks?

Yes, the skill includes specialized patterns for Chain-of-Thought and step-by-step reasoning to help the model solve complex problems more accurately.

What is few-shot learning in this context?

It is a technique where the skill helps you select and provide the best relevant examples to the model to guide its behavior for specific tasks.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: zeero

byzeero

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데이터 과학 및 ML

Enhances LLM performance and reliability through advanced techniques like Chain-of-Thought, structured outputs, and few-shot learning.

소개

The Prompt Engineering Patterns skill is a comprehensive toolkit for developers looking to maximize the effectiveness of Large Language Models in production. By implementing sophisticated strategies such as semantic few-shot selection, Pydantic-based structured outputs, and progressive disclosure, this skill provides the necessary scaffolding to build robust, predictable, and high-performing AI applications. It serves as a bridge between basic prompting and production-grade engineering, ensuring that model outputs remain consistent, controllable, and easy to integrate into existing software workflows.

주요 기능

Error recovery and fallback mechanisms for model failures
Structured JSON output enforcement via Pydantic
3 GitHub stars
Dynamic few-shot example selection using semantic similarity
Chain-of-thought reasoning patterns for complex logic
Role-based system prompt optimization templates

사용 사례

Transforming unstructured text into validated JSON data
Building production-ready LLM applications with high reliability
Optimizing complex reasoning tasks for better logical accuracy

소개

주요 기능

Error recovery and fallback mechanisms for model failures
Structured JSON output enforcement via Pydantic
3 GitHub stars
Dynamic few-shot example selection using semantic similarity
Chain-of-thought reasoning patterns for complex logic
Role-based system prompt optimization templates

사용 사례

Transforming unstructured text into validated JSON data
Building production-ready LLM applications with high reliability
Optimizing complex reasoning tasks for better logical accuracy