skills.rest
cndoit18
cndoit18COMMUNITY
Explore all skills by this author →
WebsiteGitHub

prompt-engineering-patterns

Design and optimize LLM prompts with few-shot learning and chain-of-thought.

11|1|Updated Mar 18, 2025
Search Tags:#prompt engineering#optimization#chain-of-thought#llm#few-shot learning#prompt templates
One-click install
npx skills add https://github.com/cndoit18/dotfiles --skill prompt-engineering-patterns-cndoit18
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/cndoit18/dotfiles/tree/main/home/dot_claude/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/cndoit18/dotfiles --skill prompt-engineering-patterns-cndoit18

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps users create, optimize, and manage prompts to get the best possible results from Large Language Models (LLMs), ensuring reliability and controllability in production environments.

Core Features & Use Cases

  • Advanced Prompt Techniques: Learn and apply methods like Chain-of-Thought, Few-Shot Learning, and Self-Consistency to improve LLM output quality.
  • Prompt Optimization: Systematically refine prompts for better accuracy, consistency, and efficiency.
  • Use Case: A developer needs to build a customer service chatbot that can accurately categorize user inquiries. They use this Skill to design and test prompts that reliably classify incoming messages into predefined categories like "billing inquiry," "technical support," or "feature request."

Quick Start

Use the prompt-engineering-patterns skill to generate a prompt for sentiment analysis that includes three few-shot examples.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I optimize LLM prompts for production reliability?▼

Optimize LLM prompts for production by applying few-shot learning, chain-of-thought, and prompt templating to enhance output reliability and controllability.

What is the best way to design a few-shot learning prompt for text classification?▼

Design few-shot learning prompts by providing specific examples of inputs and desired outputs to guide the LLM in accurately categorizing text into predefined groups.

How does chain-of-thought prompting improve LLM output quality?▼

Chain-of-thought prompting improves LLM output quality by guiding the model through intermediate reasoning steps, which enhances accuracy and consistency for complex tasks.

Can I use prompt templates to manage system prompt design?▼

Use prompt templates to systematically manage system prompt design, allowing iterative refinement and evaluation to ensure consistent LLM performance.

Why does my LLM prompt return inconsistent results?▼

Inconsistent LLM prompt results often occur due to a lack of structured optimization; implementing self-consistency and systematic prompt refinement can resolve this issue.

Related Skills

Explore More Software Engineering Skills→
daemon-blockint-techdaemon-blockint-tech
7

prompt-engineer

Design and evaluate LLM prompts with patterns, system prompts, and output formats.

Community
Advanced
AI-Foundry-CoreAI-Foundry-Core
4

prompt-engineering-patterns

Optimize LLM prompt strategies for reliability and controllability.

Official
Advanced
aaaa47080aaaa47080
4

cost-aware-llm-pipeline

Routes LLM calls by task complexity to optimize cost and performance using BudgetTracker and caching.

Community
Advanced
fikriaffikriaf

unsloth

Fine-tune LLMs with Unsloth using LoRA and QLoRA techniques.

Community
Advanced