prompt-engineering-patterns_skill

Overview

This skill teaches advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability for production systems. It focuses on building reusable templates, optimizing few-shot strategies, and applying structured reasoning patterns to improve accuracy and consistency. Use it to design prompts that scale with real-world constraints like token limits, latency, and verification needs.

How this skill works

It inspects prompt design across core patterns: few-shot example selection, chain-of-thought prompting, template composition, and system-level instruction. It provides concrete strategies for dynamic example retrieval, stepwise reasoning elicitation, error recovery, and token-efficient formatting. The skill also outlines validation flows and metrics to measure prompt effectiveness in production.

When to use it

• Designing or refactoring prompts for production LLM applications
• Improving output consistency, accuracy, and interpretability
• Implementing few-shot systems with dynamic example selection
• Eliciting reliable multi-step reasoning (CoT, self-consistency)
• Reducing token usage and latency while preserving quality

Best practices

• Start simple and add complexity progressively (progressive disclosure)
• Use representative, high-quality examples rather than long textual descriptions
• Measure accuracy, consistency, latency, and token usage over diverse test sets
• Move stable context to system prompts and cache common prefixes
• Version prompts and run A/B tests before deploying changes

Example use cases

• Convert natural language requirements into secure SQL queries using few-shot examples
• Build a customer support assistant with role-based system prompts and fallback behaviors
• Create RAG prompts that constrain answers to retrieved context and flag missing info
• Design a verification step that enforces output format and cites sources
• Optimize prompt templates for low-latency batch inference and streaming outputs