AI Prompt Optimization

---

name: ai-prompt-optimization description: Use when users need to optimize prompts for AI conversations, generate structured templates, create few-shot examples, design chain-of-thought guidance, or diagnose and improve existing prompts. Applicable to prompt optimization for various AI tools such as ChatGPT, Claude, Midjourney, etc.

---

AI Prompt Optimization

Core Capabilities

When users seek prompt optimization assistance, provide the following services:

1. Diagnosis & Optimization - Analyze existing prompt issues and provide specific improvement plans 2. Template Generation - Generate structured prompt templates for different scenarios 3. Few-Shot Generation - Create example-driven few-shot prompts 4. Chain-of-Thought Guidance - Design CoT (Chain of Thought) prompts

Usage

1. Diagnosis & Optimization Workflow

When a user provides a prompt for optimization:

Analyze Structure → Identify Issues → Provide Improved Version → Explain Changes

Diagnosis Checklist:

[ ] Is the role/identity clearly defined?

[ ] Is the task objective specific and clear?

[ ] Are output format/style constrained?

[ ] Is the necessary context/background information provided?

[ ] Are boundary conditions and exceptions specified?

[ ] Are there clear success criteria?

2. Template Generation

Generate structured templates based on user scenarios. Core template format:

# Role Definition
You are a [role] in [professional domain], skilled at [core competency].
Task Description
Please help me [specific task], with the goal of [expected outcome].
Context Information
Background: [relevant background]
Audience: [target users]
Constraints: [boundary conditions]
Output Requirements
Format: [desired format]
Style: [language style]
Length: [length requirement]
Quality Standards
[Key metrics for evaluating output]

3. Few-Shot Example Generation

Generate few-shot examples for complex tasks:

1. Select Representative Samples - 3-5 examples covering different variants 2. Format Examples - Input → Output structure 3. Add Explanations - Explain the rationale for selecting each example

4. Chain-of-Thought Design

Design CoT prompts for tasks requiring reasoning:

Before giving your final answer, please think through the following steps:
1. [Understand the Problem] - ...
2. [Decompose the Problem] - ...
3. [Step-by-Step Reasoning] - ...
4. [Verify the Conclusion] - ...

Scenario Reference

For complete scenario templates and examples, see references/templates.md:

Writing assistance prompts

Code generation prompts

Image generation prompts

Data analysis prompts

Q&A and consultation prompts

Optimization Principles

1. Specific > Vague - Clearly specify what is wanted and what is not 2. Structured > Scattered - Use clear segmentation and markers 3. Constrained > Free - Appropriate constraints improve output quality 4. Iterative > One-Shot - Encourage users to continuously optimize based on output

1. Diagnosis & Optimization Workflow

When a user provides a prompt for optimization:

Analyze Structure → Identify Issues → Provide Improved Version → Explain Changes

Diagnosis Checklist:

[ ] Is the role/identity clearly defined?

[ ] Is the task objective specific and clear?

[ ] Are output format/style constrained?

[ ] Is the necessary context/background information provided?

[ ] Are boundary conditions and exceptions specified?

[ ] Are there clear success criteria?

2. Template Generation

Generate structured templates based on user scenarios. Core template format:

# Role Definition
You are a [role] in [professional domain], skilled at [core competency].
Task Description
Please help me [specific task], with the goal of [expected outcome].
Context Information
Background: [relevant background]
Audience: [target users]
Constraints: [boundary conditions]
Output Requirements
Format: [desired format]
Style: [language style]
Length: [length requirement]
Quality Standards
[Key metrics for evaluating output]

3. Few-Shot Example Generation

Generate few-shot examples for complex tasks:

1. Select Representative Samples - 3-5 examples covering different variants 2. Format Examples - Input → Output structure 3. Add Explanations - Explain the rationale for selecting each example

4. Chain-of-Thought Design

Design CoT prompts for tasks requiring reasoning:

Before giving your final answer, please think through the following steps:
1. [Understand the Problem] - ...
2. [Decompose the Problem] - ...
3. [Step-by-Step Reasoning] - ...
4. [Verify the Conclusion] - ...