Prompt Optimizer

Optimize prompts for better AI model performance using engineering techniques.

When to Activate

• User wants to improve a prompt
• Token efficiency needed
• Better AI responses desired
• Prompt engineering techniques requested
• Model-specific optimization needed

Optimization Areas

1. Prompt Engineering

• Chain-of-thought reasoning
• Few-shot examples
• Role-based instructions
• Clear delimiters and formatting
• Output format specifications

2. Context Optimization

• Minimize token usage
• Hierarchical information structure
• Remove redundancy
• Add relevant context
• Compression techniques

3. Performance Testing

• Create prompt variants
• Design evaluation criteria
• Test edge cases
• Measure consistency
• Compare outputs

4. Model-Specific Optimization

• GPT-4 best practices
• Claude optimization techniques
• Prompt chaining strategies
• Temperature/parameter tuning
• Token budget management

5. RAG Integration

• Context window management
• Retrieval query optimization
• Chunk size recommendations
• Embedding strategies
• Reranking approaches

6. Production Considerations

• Prompt versioning
• A/B testing framework
• Monitoring metrics
• Fallback strategies
• Cost optimization

Output

Provides:

• Optimized prompt variants
• Explanations for each change
• Evaluation metrics
• Testing strategies
• Quality and cost considerations