Is it possible to reduce token costs using this skill?

Yes, it provides performance optimization guidelines focusing on token efficiency, such as removing redundancies and consolidating instructions without sacrificing quality.

Can I use this for RAG applications?

Absolutely. It includes specific integration patterns for combining retrieved context with optimized prompt templates for Retrieval-Augmented Generation.

How does it help with model reliability?

It implements error recovery patterns, self-verification steps, and instruction hierarchies to reduce hallucinations and ensure consistent output formats.

Does this skill support dynamic example selection?

Yes, it includes patterns for Few-Shot Learning that utilize semantic similarity and diversity sampling to select the most relevant examples for a given query.

What is the primary benefit of the Prompt Engineering Patterns skill?

It provides a standardized framework for building reliable, high-performance prompts for production environments using advanced techniques like CoT and few-shot learning.

LLM Prompt Engineering Patterns

Name: LLM Prompt Engineering Patterns
Author: EricGrill

byEricGrill

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データサイエンスとML

Implements advanced prompt engineering techniques like few-shot learning and chain-of-thought to maximize LLM performance and reliability.

概要

LLM Prompt Engineering Patterns is a comprehensive skill set designed for developers building production-grade AI applications. It provides structured frameworks for implementing complex reasoning patterns, few-shot examples, and dynamic template systems that ensure consistent, high-quality model outputs. By utilizing battle-tested patterns like Instruction Hierarchy and Progressive Disclosure, this skill helps users move beyond basic prompting to create robust, controllable, and token-efficient AI interactions optimized for production environments.

主な機能

Advanced Few-Shot Learning with dynamic example selection and semantic similarity
System prompt design patterns for specialized AI assistant behaviors and constraints
Chain-of-Thought (CoT) reasoning elicitation for complex task decomposition
Error recovery and self-verification patterns to ensure production-grade reliability
Modular prompt template systems with variable interpolation and conditional sections
2 GitHub stars

ユースケース

Reducing model hallucinations through structured reasoning traces and validation steps
Scaling prompt management via modular templates, versioning, and performance monitoring
Optimizing complex prompts for production-grade LLM applications to increase consistency

概要

主な機能

Advanced Few-Shot Learning with dynamic example selection and semantic similarity
System prompt design patterns for specialized AI assistant behaviors and constraints
Chain-of-Thought (CoT) reasoning elicitation for complex task decomposition
Error recovery and self-verification patterns to ensure production-grade reliability
Modular prompt template systems with variable interpolation and conditional sections
2 GitHub stars

ユースケース

Reducing model hallucinations through structured reasoning traces and validation steps
Scaling prompt management via modular templates, versioning, and performance monitoring
Optimizing complex prompts for production-grade LLM applications to increase consistency