Does it support dynamic few-shot learning?

Absolutely. It covers strategies for selecting the most relevant examples using semantic similarity to provide the LLM with the best context for a specific query.

How does this skill help with production LLM applications?

It provides frameworks for token efficiency, latency reduction, and error recovery to ensure stable and cost-effective performance in live environments.

What are prompt engineering patterns?

They are reusable strategies and structures, such as few-shot learning and Chain-of-Thought, used to guide LLMs toward more accurate, consistent, and controllable outputs.

Can I use this for RAG (Retrieval-Augmented Generation) systems?

Yes, it includes specific integration patterns for combining retrieved context with structured instructions to improve the accuracy and groundedness of AI responses.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.

About

This skill provides a comprehensive framework for mastering advanced prompt engineering to enhance LLM performance, reliability, and controllability in production environments. It equips developers with structured patterns—including few-shot learning, chain-of-thought reasoning, and modular template systems—to build sophisticated AI applications that produce consistent, high-quality results. Whether you are debugging inconsistent outputs or designing complex multi-step reasoning workflows, this skill offers the tools and best practices necessary to maximize the potential of modern large language models.

Key Features

Guides the design of robust system prompts and error recovery instructions.
0 GitHub stars
Offers modular prompt template systems with variable interpolation and logic.
Implements structured reasoning patterns like Chain-of-Thought and Tree-of-Thought.
Provides dynamic few-shot learning strategies with semantic similarity selection.
Includes systematic prompt optimization and A/B testing workflows.

Use Cases

Optimizing token usage and reducing latency in complex AI workflows.
Building production-grade LLM applications that require high reliability.
Refining inconsistent AI outputs through structured reasoning and few-shot examples.

About

Key Features

Guides the design of robust system prompts and error recovery instructions.
0 GitHub stars
Offers modular prompt template systems with variable interpolation and logic.
Implements structured reasoning patterns like Chain-of-Thought and Tree-of-Thought.
Provides dynamic few-shot learning strategies with semantic similarity selection.
Includes systematic prompt optimization and A/B testing workflows.

Use Cases

Optimizing token usage and reducing latency in complex AI workflows.
Building production-grade LLM applications that require high reliability.
Refining inconsistent AI outputs through structured reasoning and few-shot examples.