Advanced Prompt Optimization
Systematically optimize prompts: automated testing, genetic algorithms, prompt compression, and performance tuning.
TL;DR
Optimize prompts systematically: build evaluation sets, test variations, use automated optimization (genetic algorithms, gradient-based), compress prompts, and measure performance scientifically.
Systematic optimization process
- Define success metrics
- Build evaluation dataset (100-1000 examples)
- Establish baseline
- Generate variations
- Test and measure
- Iterate on best performers
Automated optimization
DSPy: Prompt optimization via program synthesis
PromptBench: Benchmark and optimize prompts
Genetic algorithms: Evolve prompts over generations
Gradient-based (soft prompts): Optimize continuous embeddings
Prompt compression
Remove unnecessary tokens while preserving performance:
A/B testing
- Random assignment
- Statistical significance testing
- Track business metrics
- Multi-armed bandits for continuous optimization
Metrics to optimize
- Task accuracy
- Latency
- Cost (tokens used)
- User satisfaction
- Refusal rate (too many "I can't do that")
Common optimizations
- Simplify language
- Add examples strategically
- Remove redundancy
- Use structured formats
- Optimize few-shot selection
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