Optimization improves a generative AI application toward better quality, efficiency, scalability, or cost. In prompt management, it means continuously refining prompts using performance data while controlling token use and response complexity. section 10.3.4 section 11.5.1
Practical optimization techniques include:
- Track accuracy, relevance, user feedback, response quality, latency, throughput, and token usage before changing the system. section 10.3.4 section 11.5.1
- Refine prompts by removing unnecessary text, reducing complexity, and producing shorter outputs without compromising quality. section 10.3.4 section 11.5.1
- Use caching for frequently requested information to reduce latency and computational load. section 11.5.1
- Distribute traffic with load balancing and adjust capacity through auto-scaling when demand changes. section 11.5.1
- Control spending with token monitoring, quotas, or rate limiting to prevent unexpected usage spikes. section 11.5.1
Production optimization loop
Measure an application's quality, relevance, latency, throughput, and token usage; refine prompts or outputs where results fall short; then test the application under different loads. Prometheus and Grafana can support metric collection and visualization, while Apache JMeter or Locust can simulate multiple users. Continue monitoring real-world data and adjust caching, capacity, and usage controls as demand changes. section 10.3.4 section 11.5.1
