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prompt-engineering-research

Research prompt engineering best practices for AI image generation and LLM-as-judge scoring.

Updated Feb 23, 2026
Search Tags:#prompt engineering#image generation#prompt optimization#AI research#LLM as judge#template improvement
One-click install
npx skills add https://github.com/NikGor/image-prompt-optimizer --skill prompt-engineering-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-research
Source: https://github.com/NikGor/image-prompt-optimizer/tree/main/.claude/skills/prompt-engineering-research
Command: npx skills add https://github.com/NikGor/image-prompt-optimizer --skill prompt-engineering-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses challenges in generating high-quality AI images by researching and applying best practices for prompt engineering and improving LLM-as-judge scoring.

Core Features & Use Cases

  • Prompt Structure Research: Investigates optimal prompt elements for image generation models like DALL-E 3 and Grok Aurora.
  • LLM-as-Judge Calibration: Explores patterns to ensure accurate and well-calibrated scoring from AI judges.
  • Iterative Revision Strategies: Develops methods for refining prompts based on feedback and evaluation.
  • Use Case: When image generation results are consistently poor or judge scores are too narrow, this Skill provides the research needed to improve prompt templates and evaluation logic.

Quick Start

Use the prompt-engineering-research skill to research DALL-E 3 prompt engineering best practices.

Frequently Asked Questions about prompt-engineering-research

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

FAQPage Schema
How do I improve AI image generation prompts for DALL-E 3?▼

To improve AI image generation prompts, research optimal prompt elements and iterative revision strategies. This skill investigates best practices to refine Jinja2 prompt templates and optimize generation loops for models like DALL-E 3.

What is LLM-as-judge calibration for evaluating images?▼

LLM-as-judge calibration is the process of exploring scoring patterns to ensure accurate AI evaluations. It addresses issues like narrow score distributions by researching and applying calibration logic to your evaluation templates.

How do I fix narrow judge scores in my prompt evaluation loop?▼

To fix narrow judge scores in your prompt evaluation loop, apply LLM-as-judge calibration patterns. This skill researches scoring best practices to adjust your evaluation logic and achieve well-calibrated, accurate feedback.

Does this prompt optimization research apply to Grok Aurora?▼

Yes, this prompt optimization research applies to Grok Aurora. It investigates optimal prompt structures specifically for image generation models like DALL-E 3 and Grok Aurora to enhance generation results.

What's the best way to structure prompt templates for AI image generation?▼

The best way to structure prompt templates for AI image generation is by applying researched best practices to Jinja2 templates. This skill gathers information using WebSearch and WebFetch to refine your iterative revision strategies.

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