What reasoning techniques are included?

It covers Chain-of-Thought (CoT), zero-shot reasoning elicitation, and self-consistency techniques to ensure the model follows a logical path to its conclusion.

Can I use these patterns for production applications?

Yes, it is specifically designed for production-grade prompts, focusing on token efficiency, latency reduction, and robust error handling for real-world scaling.

How do I reduce token usage with this skill?

The skill provides patterns for token efficiency, such as removing redundant instructions and consolidating prompts without sacrificing the quality of the output.

Does it support dynamic data in prompts?

Absolutely. It includes patterns for template interpolation and dynamic example retrieval based on semantic similarity to the user's current query.

How does this skill improve LLM reliability?

It provides structured patterns like few-shot examples and self-verification steps to ensure outputs remain consistent and accurate across different user inputs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: drgaciw

bydrgaciw

데이터 과학 및 ML

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.

소개

This skill provides a comprehensive framework for designing, optimizing, and managing complex prompts for large language models. It covers essential strategies like Few-Shot Learning, Chain-of-Thought reasoning, and dynamic template interpolation, enabling developers to build more consistent and reliable AI-driven features. By implementing these battle-tested patterns, users can reduce token costs, improve response quality, and handle edge cases more effectively in production environments where precision and consistency are critical.

주요 기능

Structured reasoning patterns including Chain-of-Thought (CoT)
Modular prompt template systems with variable interpolation
Few-shot learning with dynamic example selection strategies
Performance optimization and iterative refinement workflows
Robust error recovery and fallback instruction patterns
0 GitHub stars

사용 사례

Building reliable code generation systems using few-shot examples
Designing complex system prompts for specialized AI assistants
Optimizing production LLM application costs and consistency

소개

주요 기능

Structured reasoning patterns including Chain-of-Thought (CoT)
Modular prompt template systems with variable interpolation
Few-shot learning with dynamic example selection strategies
Performance optimization and iterative refinement workflows
Robust error recovery and fallback instruction patterns
0 GitHub stars

사용 사례

Building reliable code generation systems using few-shot examples
Designing complex system prompts for specialized AI assistants
Optimizing production LLM application costs and consistency