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#

ParameterDescriptionDefaultgoalWhat the prompt should achieveInferred from contentmodelTarget LLM (affects strategy)General-purposemax_tokensTarget output lengthNo limitstyle``concise / detailed / creative``detailed``iterationsHow many refinement passes1

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#

text

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.