Does it support complex logical reasoning?

Absolutely. It implements Chain-of-Thought and multi-step verification techniques to help the model break down complex tasks into manageable, logical steps.

Can I use these patterns with RAG systems?

Yes, the skill includes specific integration patterns for combining retrieved context with optimized prompts to improve the accuracy of grounded AI responses.

What are prompt engineering patterns?

Prompt engineering patterns are reusable techniques and structured approaches, such as Few-Shot learning or Chain-of-Thought, designed to improve the quality, logic, and reliability of LLM outputs.

How does this skill help with production LLM applications?

It provides standardized frameworks for testing, optimizing, and versioning prompts, ensuring that your AI application remains consistent and performant under various real-world inputs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: amurata

byamurata

•

Data Science & ML

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

About

Prompt Engineering Patterns provides a comprehensive framework for mastering LLM performance, reliability, and control in production environments. It empowers developers to implement sophisticated patterns such as dynamic few-shot learning, chain-of-thought reasoning, and modular template systems to ensure consistent and high-quality outputs. This skill is particularly valuable when building complex AI assistants, debugging inconsistent model behaviors, or optimizing token usage without sacrificing accuracy in large-scale applications.

Key Features

Dynamic few-shot learning with semantic similarity selection
Structured reasoning patterns including Chain-of-Thought and Self-consistency
Modular prompt template system with variable interpolation
Iterative prompt optimization and A/B testing workflows
Advanced system prompt design for behavioral constraints and safety
3 GitHub stars

Use Cases

Designing robust prompts for production-grade LLM applications
Implementing specialized AI assistants with specific role-based behaviors
Debugging and refining prompts that produce inconsistent or hallucinated outputs

About

Key Features

Dynamic few-shot learning with semantic similarity selection
Structured reasoning patterns including Chain-of-Thought and Self-consistency
Modular prompt template system with variable interpolation
Iterative prompt optimization and A/B testing workflows
Advanced system prompt design for behavioral constraints and safety
3 GitHub stars

Use Cases

Designing robust prompts for production-grade LLM applications
Implementing specialized AI assistants with specific role-based behaviors
Debugging and refining prompts that produce inconsistent or hallucinated outputs