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prompt-engineering

Optimize LLM prompts with few-shot examples and chain-of-thought templates.

Updated Apr 5, 2026
Search Tags:#chain-of-thought#prompt-engineering#system-prompts#prompt-optimization#template-systems#few-shot-learning#llm-performance
One-click install
npx skills add https://github.com/rizaldiem/digital-invitation-web_V2 --skill prompt-engineering-rizaldiem
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/rizaldiem/digital-invitation-web_V2/tree/main/.windsurf/skills/prompt-engineering
Command: npx skills add https://github.com/rizaldiem/digital-invitation-web_V2 --skill prompt-engineering-rizaldiem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill reduces the trial-and-error burden of designing prompts by providing repeatable patterns, validation workflows, and optimization frameworks that improve accuracy, consistency, and cost-efficiency of LLM-driven tasks.

Core Features & Use Cases

  • Few-Shot Example Selection: Semantic similarity and diversity sampling strategies for selecting 3-5 high-impact examples.
  • Chain-of-Thought Templates: Structured CoT patterns to elicit step-by-step reasoning and self-consistency checks.
  • Prompt Optimization & Monitoring: Iterative A/B testing, performance metrics (accuracy, consistency, token efficiency, latency), and rollback strategies for production prompts.
  • Template Systems & System Prompts: Modular templates, conditional sections, and system prompt frameworks for consistent behavior across models.
  • Use Case: Optimize a customer-support classification pipeline by crafting few-shot prompts, running controlled A/B tests, and deploying the best prompt with continuous monitoring to maintain >90% accuracy.

Quick Start

Draft an optimized few-shot prompt using three diverse examples and a chain-of-thought template, specify the required output format and validation criteria.

Frequently Asked Questions about prompt-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I improve LLM accuracy and consistency with prompt engineering?▼

Improve LLM accuracy and consistency using prompt engineering by applying chain-of-thought templates, few-shot example selection, and iterative A/B testing to validate output performance and token efficiency.

What is the best way to structure few-shot examples for large language models?▼

The best way to structure few-shot examples for large language models is selecting 3-5 high-impact examples using semantic similarity and diversity sampling to elicit accurate reasoning and consistent task performance.

How do I set up A/B testing for production-scale prompt optimization?▼

Set up A/B testing for production-scale prompt optimization by defining performance metrics like accuracy, consistency, and token efficiency, then running controlled tests with rollback strategies to deploy the best prompt.

Can I use chain-of-thought templates for multi-step analysis tasks?▼

Yes, you can use chain-of-thought templates for multi-step analysis tasks to elicit step-by-step reasoning and self-consistency checks, ensuring reliable transformation and classification outputs across different models.

When should I use template systems and system prompts for LLM tasks?▼

Use template systems and system prompts for LLM tasks when you need consistent behavior across models, requiring modular templates and conditional sections to enforce safety constraints and model-specific formatting.

Why does my LLM classification pipeline have inconsistent reasoning outputs?▼

Your LLM classification pipeline has inconsistent reasoning outputs due to lacking structured prompt patterns, which can be fixed by implementing chain-of-thought templates and semantic few-shot example selection.

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