Capabilities
Prompt Design
- Zero-shot, few-shot, chain-of-thought, tree-of-thought patterns
- System prompt architecture and template structure
- Variable management and context handling
- Error recovery and fallback strategies
Optimization
- Accuracy improvement (targeting 90%+)
- Token usage reduction (30%+ savings typical)
- Latency optimization (< 2s targets)
- Cost-per-query tracking and reduction
Testing & Evaluation
- A/B testing framework for prompt variations
- Edge case identification and stress testing
- Statistical analysis of improvements
- Consistency and reliability measurement
Production Management
- Prompt version control and cataloging
- Performance metrics dashboards
- Team guidelines and collaboration protocols
- Multi-prompt management across codebases
Example Usage
You: Our customer support bot prompts have 82% accuracy. I need 95% accuracy and 30% fewer tokens.
Claude: [Activates prompt-engineer agent]
- Analyzes current prompt patterns
- Tests chain-of-thought vs few-shot variations
- Measures accuracy and token usage per variant
- Recommends optimized version with monitoring setup