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prompt-engineering

Design and evaluate LLM prompts resistant to hallucinations and injection attacks.

6|3|Updated Jan 29, 2026
Search Tags:#security#AI interaction#prompt design#robustness#LLM prompts
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill prompt-engineering-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/skills/prompt-engineering
Command: npx skills add https://github.com/jonnabio/ace-framework --skill prompt-engineering-jonnabio

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of creating reliable, secure, and highly-performant prompts for Large Language Models, ensuring consistent and desired output formats and behaviors.

Core Features & Use Cases

  • Prompt Construction: Guides for designing, evaluating, and versioning LLM prompts.
  • Mitigation Strategies: Techniques to prevent hallucinations and defend against prompt injection.
  • Use Case: When developing an AI application that requires interaction with an LLM, this Skill helps create effective prompts to drive desired outcomes.

Quick Start

Apply the prompt-engineering skill to design the extraction prompt for the invoice processing feature.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I design LLM prompts that prevent hallucinations and prompt injection attacks?▼

To prevent hallucinations and prompt injection, this Skill provides techniques to construct structured, secure LLM prompts that enforce consistent output formats and resist malicious inputs.

What is the best way to structure prompts for consistent LLM output formats?▼

Structured prompt construction ensures consistent LLM output by applying design and evaluation techniques that enforce stable, desired behaviors and formats across various interactions.

Can I use prompt engineering techniques for any Large Language Model interaction?▼

Yes, the prompt design and mitigation strategies are applicable to any LLM interaction, ensuring robust and secure output regardless of the specific underlying Large Language Model platform.

How do I evaluate and version LLM prompts during AI application development?▼

You can evaluate and version LLM prompts by using provided construction guides to test robustness and consistency, ensuring reliable AI application behavior before deployment.

Why does my LLM prompt produce inconsistent or insecure output?▼

Insecure or inconsistent LLM output typically results from unstructured prompts lacking mitigation strategies, which secure prompt design specifically addresses to ensure robust interactions.

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