How does Chain-of-Thought (CoT) help in AI development?

CoT encourages the model to generate intermediate reasoning steps, which significantly improves performance on complex tasks involving logic, math, or multi-step processes.

Does this skill support Few-Shot learning?

Yes, it includes advanced strategies for example selection, semantic similarity retrieval, and balancing demonstration counts within context windows.

What are prompt engineering patterns?

They are structured methods and architectural templates used to guide an LLM's reasoning process and output format to ensure higher accuracy and reliability.

Can these patterns be integrated into production environments?

Absolutely. These patterns focus on maximizing controllability, reducing latency, and managing token efficiency for scalable, production-grade AI applications.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: Tahir-yamin

byTahir-yamin

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数据科学与机器学习

Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and structured output quality.

关于

This skill provides a comprehensive framework for mastering advanced prompt engineering within AI-driven applications. It enables developers to implement structured reasoning patterns like Chain-of-Thought, manage dynamic few-shot learning systems, and create robust template architectures. By applying these battle-tested patterns, users can minimize hallucinations, ensure consistent outputs, and optimize token efficiency for production-grade LLM integrations and complex AI workflows.

主要功能

Modular prompt template systems
3 GitHub stars
Chain-of-Thought reasoning elicitation
Iterative prompt optimization workflows
Dynamic few-shot learning strategies
Automated error recovery patterns

使用场景

Building specialized AI assistants with custom system prompts
Improving RAG performance through context-aware prompting
Optimizing complex production LLM prompts for consistency

关于

主要功能

Modular prompt template systems
3 GitHub stars
Chain-of-Thought reasoning elicitation
Iterative prompt optimization workflows
Dynamic few-shot learning strategies
Automated error recovery patterns

使用场景

Building specialized AI assistants with custom system prompts
Improving RAG performance through context-aware prompting
Optimizing complex production LLM prompts for consistency