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prompt-engineering

Guide prompt optimization with few-shot learning and chain-of-thought patterns.

Updated Jan 15, 2026
Search Tags:#prompt engineering#optimization#chain-of-thought#ai#llm#few-shot learning
One-click install
npx skills add https://github.com/lips85/antigravity-skills-bootstrap --skill prompt-engineering-lips85
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/lips85/antigravity-skills-bootstrap/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/lips85/antigravity-skills-bootstrap --skill prompt-engineering-lips85

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users improve their interactions with AI models by providing expert guidance on crafting effective prompts, leading to more accurate, reliable, and controlled outputs.

Core Features & Use Cases

  • Prompt Optimization: Learn techniques to refine prompts for better performance and efficiency.
  • Advanced Patterns: Understand and implement methods like Few-Shot Learning, Chain-of-Thought, and Template Systems.
  • Use Case: If an AI agent is not understanding your requests correctly or producing suboptimal results, use this Skill to learn how to rephrase your instructions and provide better examples to guide the AI.

Quick Start

Use the prompt engineering skill to learn about few-shot learning patterns.

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 response quality when the AI misunderstands my requests?▼

Prompt optimization refines instructions by implementing structured patterns like few-shot learning and chain-of-thought, providing the AI with better examples and rephrased directives to enhance response performance and efficiency.

What is few-shot learning and how does it work for large language models?▼

Few-shot learning is a prompt engineering pattern where you provide the large language model with a few targeted examples within the prompt, conditioning the AI to generate outputs that match your desired format and reasoning.

How do I debug AI agent behavior producing suboptimal results?▼

Debug AI agent behavior by applying structured prompting strategies, utilizing system prompt design principles and chain-of-thought reasoning to trace logic errors and rephrase instructions for better agent comprehension.

Do I need to understand system prompt design to use chain-of-thought techniques?▼

Understanding system prompt design is required to effectively implement advanced patterns like chain-of-thought, as it provides the foundational principles needed to structure instructions and debug agent behavior successfully.

What's the best way to structure prompts for complex reasoning tasks?▼

The best way to structure prompts for complex reasoning is using chain-of-thought patterns, which break down logic step-by-step, combined with few-shot examples to guide the large language model through the required reasoning process.

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