Prompt Optimizer#

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

VagueOptimized"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-PatternProblemFix"Just make it good"No quality criteriaDefine what "good" means with measurable criteriaGiant wall of textAI loses focusBreak into numbered sections with clear headersContradictory instructionsAI guesses priorityRemove conflicts; rank priorities explicitlyMissing negative constraintsAI makes unwanted assumptionsAdd "Do NOT..." instructions for known failure modesNo examplesAI style is unpredictableProvide 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