Optimize Prompt

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

• Techniques for the target model family, checked against its current API rather than recalled
• Prompt chaining strategies
• Sampling parameter tuning — parameters are provider-specific. frequency_penalty and presence_penalty are OpenAI-only and are rejected by the Anthropic Messages API. On the Claude 5 family, temperature, top_p, and top_k are removed entirely — sending any of them returns a 400 whether or not thinking is enabled, so never recommend them for a Claude 5 target. budget_tokens is also rejected (use thinking: {type: "adaptive"}).
• 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