What is workload-specific artificial intelligence evaluation?
Definition
Workload-specific artificial intelligence evaluation builds tests from the actual distribution of requests an application expects. It measures the qualities that matter for those tasks, including correctness, tool use, latency, cost, reliability and user preferences.
This approach can produce different conclusions from a public leaderboard because aggregate benchmarks may overrepresent unrelated tasks. Teams should keep representative samples current and compare complete routed workflows, not only isolated model responses.
Acronyms and aliases
application-specific AI evaluation synonymworkload-specific AI evaluation variant
General terms
Frequently asked questions
Why evaluate model routing on an organization's own workload?
The organization's task mix, tools, quality needs and operating constraints can differ substantially from public benchmarks.
What should workload-specific routing evaluation measure?
It should measure task success, quality, latency, cost, reliability, fallback behavior and relevant user preferences.
