Prompt Evolution Engine

Iteratively improve AI prompts by analyzing, rewriting, comparing, and refining them using structured patterns for clarity, structure, and format compliance.

Prompt Optimizer

Iteratively improve AI prompts through structured evaluation, A/B testing, and feedback-driven refinement. Use when a prompt underperforms, produces inconsistent results, or needs optimization for a specific use case.

Usage

text

Optimize this prompt: [paste your prompt]

Or with context:

text

Optimize this prompt for [goal]. Current issues: [problems]. Target model: [model name].

How It Works

1. Analyze — identify structural weaknesses (vague instructions, missing constraints, poor examples)
2. Rewrite — apply proven prompt engineering patterns (chain-of-thought, few-shot, role-setting, output format)
3. Compare — generate before/after evaluation with expected improvement areas
4. Iterate — if user provides feedback on the rewritten prompt, refine further

Optimization Patterns Applied

• Clarity: Replace ambiguous language with specific, measurable instructions
• Structure: Add section headers, numbered steps, output format templates
• Constraints: Add boundaries (length, tone, forbidden patterns, edge cases)
• Examples: Generate few-shot examples if missing
• Chain-of-thought: Add reasoning steps for complex tasks
• Role/persona: Set context-appropriate expertise framing
• Output anchoring: Specify exact output format (JSON, markdown, etc.)

Parameters

Parameter	Description	Default
goal	What the prompt should achieve	Inferred from content
model	Target LLM (affects strategy)	General-purpose
max_tokens	Target output length	No limit
style	concise / detailed / creative	detailed
iterations	How many refinement passes	1

Output Format

markdown

## Analysis
[Weaknesses identified in original prompt]

## Optimized Prompt
[The improved prompt, ready to copy-paste]

## Changes Made
[Bullet list of specific improvements and why]

## Expected Impact
[What should improve: consistency, accuracy, relevance, format compliance]

Advanced Usage

Batch Optimization

text

Optimize these 3 prompts for the same task, pick the best approach:
1. [prompt A]
2. [prompt B]  
3. [prompt C]

A/B Test Design

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Create an A/B test for this prompt. Generate variant A (structured) and variant B (conversational). Include 5 test inputs to compare.

Model-Specific Tuning

text

Optimize this prompt specifically for Claude Sonnet 4.6. Use extended thinking triggers and XML tags.