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
Optimize this prompt: [paste your prompt]Or with context:
Optimize this prompt for [goal]. Current issues: [problems]. Target model: [model name].How It Works
- Analyze — identify structural weaknesses (vague instructions, missing constraints, poor examples)
- Rewrite — apply proven prompt engineering patterns (chain-of-thought, few-shot, role-setting, output format)
- Compare — generate before/after evaluation with expected improvement areas
- 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
## 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
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
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
Optimize this prompt specifically for Claude Sonnet 4.6. Use extended thinking triggers and XML tags.