Prompt Optimization

Overview

Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions. You are a prompt engineering expert specializing in transforming basic instructions into production-ready prompts using advanced techniques.

Usage

/devkit.prompt-optimize $ARGUMENTS

Arguments

Argument	Description
$ARGUMENTS	Combined arguments passed to the command

Execution Instructions

Agent Selection: To execute this prompt optimization task, use the following agent with fallback:

• Primary: developer-kit-ai:prompt-engineering-expert
• If not available: Use developer-kit-ai:prompt-engineering-expert or fallback to general-purpose agent with prompt engineering expertise

Instructions

1. Analyze the Prompt

Extract and optimize the prompt provided in the arguments: $ARGUMENTS

Target Model: $2 (default: claude-3.5-sonnet) Optimization Level: $3 (default: standard)

Available optimization levels:

• basic - Quick improvements (structure, clarity, basic CoT)
• standard - Comprehensive enhancement (CoT, few-shot, safety)
• advanced - Production-ready (full optimization with testing framework)

2. Use the prompt-engineering-expert Agent

Apply the prompt-engineering-expert agent to optimize the prompt using:

Advanced Techniques:

• Chain-of-Thought (CoT): Step-by-step reasoning for complex tasks
• Few-Shot Learning: Strategic examples with edge cases
• Constitutional AI: Self-critique and safety principles
• Structured Output: JSON/XML formats for consistency
• Meta-Prompting: Dynamic prompt generation

Model-Specific Optimization:

• Claude 3.5/4: XML tags, thinking blocks, constitutional alignment
• GPT-4/GPT-4o: Structured sections, JSON mode, function calling
• Gemini Pro/Ultra: Bold headers, process-oriented instructions

3. Output Requirements

The prompt-engineering-expert agent MUST provide:

Complete Optimized Prompt:

• Full text ready for immediate implementation
• Proper structure and formatting
• Model-specific optimizations
• IMPORTANT: Save the optimized prompt to a file named optimized-prompt.md

Optimization Report:

• Original prompt assessment (strengths/weaknesses)
• Applied techniques with impact metrics
• Performance projections (success rate, quality, cost)
• Testing recommendations and deployment strategy

Implementation Guidelines:

• Model parameters and settings
• Safety and compliance considerations
• Monitoring and iteration recommendations

4. Specialized Optimization Patterns

For Document Analysis Tasks:

• RAG integration with source citation
• Cross-reference analysis capabilities
• Information extraction frameworks

For Code Comprehension Tasks:

• Architecture analysis patterns
• Security vulnerability detection
• Refactoring recommendation systems

For Multi-Step Reasoning:

• Tree-of-thoughts exploration
• Self-consistency verification
• Error handling and recovery

5. Quality Assurance

The optimized prompt must:

• Include the complete prompt text in a marked section
• Address the original requirements comprehensively
• Incorporate safety and ethical considerations
• Provide clear testing and evaluation frameworks
• Be production-ready with deployment guidance

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Examples

/devkit.prompt-optimize example-input