Prompt Optimizer

Refine vague or poorly structured prompts into clear, structured, and effective prompts using proven prompt engineering techniques for improved AI output qua...

Transform vague, underperforming prompts into precise, structured prompts that consistently produce high-quality AI outputs.

Description

This skill takes any user prompt — whether vague, ambiguous, or poorly structured — and systematically refines it into a professional-grade prompt following established prompt engineering principles. It applies techniques from chain-of-thought, role-prompting, few-shot learning, and structured output formatting to maximize AI performance.

When to Use

• The user provides a vague prompt like "write something about marketing" and expects better results
• A prompt produces inconsistent or off-topic outputs
• Converting natural language requests into structured prompts
• Building prompt templates for repeated use
• Debugging prompts that fail in edge cases

Instructions

The OPTIMIZE Framework

When refining a prompt, apply these six principles in order:

O — Objective (明确目标)

Problem: Vague verbs like "write about," "explain," "help with" Fix: Specify exact deliverable and success criteria

Vague	Optimized
"Write about AI"	"Write a 500-word blog post explaining how large language models work, targeting software developers with 2+ years of experience"
"Make it better"	"Improve clarity and reduce sentence length by 30% while preserving all technical details"
"Fix the code"	"Refactor this Python function to reduce cyclomatic complexity below 5 and add type hints"

P — Persona (设定角色)

Assign a specific role to ground the AI's expertise:

• "You are a senior staff engineer at Google with 15 years of distributed systems experience"
• "You are a Nature journal reviewer specializing in immunology"
• "You are a direct-response copywriter trained by Eugene Schwartz's methods"

Include constraints: "Respond only with what you're confident about. If uncertain, say so."

T — Task Structure (任务结构)

Break complex tasks into ordered steps:

text

1. First, analyze X and identify Y
2. Then, based on Y, generate Z using method A
3. Finally, format the output as...

For multi-step tasks, use numbered steps rather than one compound instruction.

I — Input Specification (输入规范)

Define what the user will provide:

• "I will provide: (1) a product description, (2) target audience, (3) competitor list"
• "Input: A CSV file with columns [date, revenue, expenses]"
• "Here is the code to review: <language>\n...\n"

Explicit input templates reduce ambiguity.

M — Metrics & Constraints (约束条件)

Add specific constraints:

text

Constraints:
- Maximum 500 words
- Use only peer-reviewed sources
- No jargon; explain all technical terms
- Output in Chinese
- Format as a comparison table
- Must include 3 concrete examples

I — Ideal Output (理想输出)

Show or describe the desired output format:

• Provide an example of expected output (few-shot)
• Specify format: JSON schema, markdown table, numbered list, code block
• Define evaluation criteria: "The output is successful if a non-expert can understand the explanation"

Prompt Optimization Process

Given a raw prompt, produce:

1. Diagnosis: What's wrong with the original (vague goal? missing context? no format? no constraints?)
2. Optimized Prompt: The refined version following OPTIMIZE framework
3. Explanation: What was changed and why

Common Anti-Patterns

Anti-Pattern	Problem	Fix
"Just make it good"	No quality criteria	Define what "good" means with measurable criteria
Giant wall of text	AI loses focus	Break into numbered sections with clear headers
Contradictory instructions	AI guesses priority	Remove conflicts; rank priorities explicitly
Missing negative constraints	AI makes unwanted assumptions	Add "Do NOT..." instructions for known failure modes
No examples	AI style is unpredictable	Provide 1-3 input/output examples

Examples

Raw Prompt: "Write an email to my boss asking for a raise"

Optimized Prompt:

text

You are a professional career coach helping a software engineer draft a salary negotiation email.

Task: Write a salary increase request email to my manager.

Context:
- I'm a mid-level software engineer, 2 years at the company
- I recently led a project that saved the company $200K annually
- The company just closed a successful funding round
- My current salary is below market rate based on Levels.fyi data

Requirements:
- Professional but warm tone (not aggressive, not passive)
- 150-250 words
- Lead with value delivered, not personal needs
- Include a specific meeting request
- No ultimatums or comparisons with colleagues

Format: Standard email with subject line

Raw Prompt: "分析这个数据"

Optimized Prompt:

text

You are a senior data analyst. Analyze the provided dataset and produce a business report.

Input: I will provide a CSV file with monthly sales data (columns: date, product, quantity, revenue, region).

Steps:
1. Identify the top 3 revenue-generating products
2. Detect any seasonal trends or anomalies
3. Compare regional performance
4. Provide 3 actionable business recommendations

Output format:
- Executive summary (3 sentences)
- Key findings as a numbered list
- Recommendations with expected impact (high/medium/low)
- Any data quality concerns

Language: Chinese

Tips

• The best prompts read like briefs given to a competent professional, not commands given to a machine
• Always test optimized prompts with edge cases before standardizing
• Keep prompts under 500 words when possible — longer prompts can confuse the model
• Version your prompts (v1, v2) and track which versions produce better results
• When a prompt still fails after optimization, the task may need to be decomposed into subtasks