How do I reduce API costs using these patterns?

The skill includes token efficiency strategies such as instruction consolidation, removing redundant phrases, and moving stable content into system prompts.

Does it support complex output formats like JSON or SQL?

Yes, it provides system prompt design strategies and few-shot example patterns specifically tailored for enforcing strict structural requirements in AI responses.

Can I use these patterns for RAG systems?

Yes, the skill includes specific integration patterns for combining retrieved knowledge with optimized prompt templates to reduce hallucinations and improve factual accuracy.

What are prompt engineering patterns?

Prompt engineering patterns are standardized, reusable techniques like few-shot learning, chain-of-thought, and progressive disclosure used to systematically improve the accuracy and reliability of LLM outputs.

How does this skill help with production LLM applications?

It provides methods for versioning, A/B testing, and optimizing prompts to ensure they perform consistently at scale while managing token costs and latency.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

Ciencia de Datos y ML

Implement advanced LLM prompting techniques like few-shot learning and chain-of-thought to enhance production AI reliability and output quality.

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This skill provides a comprehensive framework for designing, optimizing, and managing complex LLM prompts within production environments. It equips developers with standardized patterns for structured reasoning, dynamic few-shot example selection, and template-based prompt composition, ensuring AI outputs remain consistent, controllable, and cost-efficient. Whether you are building sophisticated RAG systems or specialized AI assistants, this skill offers the technical methodology and implementation patterns needed to bridge the gap between basic prompting and production-grade LLM integration.

Características Principales

Systematic prompt optimization workflows for iterative performance refinement
Specialized system prompt design for strict behavioral constraints and safety guidelines
Modular prompt template systems with variable interpolation and conditional logic
Advanced reasoning patterns including Chain-of-Thought and Tree-of-Thought elicitation
Dynamic few-shot learning with semantic similarity example selection
0 GitHub stars

Casos de Uso

Optimizing token usage and reducing latency in high-volume, cost-sensitive LLM deployments
Creating specialized AI agents with strict adherence to specific JSON or SQL output formats
Developing production-ready RAG applications that require high-reliability context processing

Acerca de

Características Principales

Systematic prompt optimization workflows for iterative performance refinement
Specialized system prompt design for strict behavioral constraints and safety guidelines
Modular prompt template systems with variable interpolation and conditional logic
Advanced reasoning patterns including Chain-of-Thought and Tree-of-Thought elicitation
Dynamic few-shot learning with semantic similarity example selection
0 GitHub stars

Casos de Uso

Optimizing token usage and reducing latency in high-volume, cost-sensitive LLM deployments
Creating specialized AI agents with strict adherence to specific JSON or SQL output formats
Developing production-ready RAG applications that require high-reliability context processing