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prompt-alignment

Refine AI prompts through iterative fit-to-generalize testing against input-output pairs.

Updated Aug 27, 2026
Search Tags:#prompt engineering#llm#generalization#iterative refinement#prompt tuning#ai alignment
One-click install
npx skills add https://github.com/mshuffett/dotfiles --skill prompt-alignment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-alignment
Source: https://github.com/mshuffett/dotfiles/tree/main/agents/skills/prompt-alignment
Command: npx skills add https://github.com/mshuffett/dotfiles --skill prompt-alignment

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of ensuring AI prompts consistently produce desired outputs, aligning them with specific rules and improving their generalization capabilities.

Core Features & Use Cases

  • Iterative Prompt Refinement: Automatically adjusts prompts based on input-output examples to improve accuracy and adherence to guidelines.
  • Generalization Testing: Probes prompts with varied inputs to ensure they perform reliably beyond the training examples.
  • Use Case: When an AI assistant's responses are slightly off-topic or miss a specific constraint, this Skill can be used to fine-tune the system prompt until the desired behavior is achieved consistently.

Quick Start

Use the prompt-alignment skill to refine the system prompt for the 'summarization' task.

Frequently Asked Questions about prompt-alignment

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

FAQPage Schema
How do I refine AI prompts to generalize to unseen inputs?▼

Prompt refinement uses an iterative fit-to-generalize loop to adjust prompts based on input-output examples, ensuring outputs align with rules and generalize reliably to unseen cases.

What is the best way to test LLM prompts against adversarial examples?▼

Testing LLM prompts against adversarial examples involves probing prompt performance with varied inputs to verify robustness and reliability beyond known training examples while adhering to safety constraints.

Why does my AI assistant miss specific constraints in the system prompt?▼

AI assistants miss constraints when prompts lack proper alignment, requiring iterative prompt tuning based on known input-output pairs to achieve the desired behavior consistently.

Can I use iterative prompt tuning for AI alignment tasks?▼

Iterative prompt tuning supports AI alignment by analyzing prompt performance against known input-output pairs and probing for robustness, ensuring outputs adhere to global safety and policy constraints.

Do I need input-output examples for prompt engineering and refinement?▼

Input-output examples are required for prompt engineering and refinement to automatically adjust prompts, analyze performance, and ensure outputs align with specified rules during the iterative process.

What are the limitations of iterative prompt refinement for LLMs?▼

Limitations of iterative prompt refinement include the need for extensive iterative testing and documentation of changes for acceptance, requiring repeated probing to ensure robustness against adversarial examples.

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