Section 171 · Chapter 20, The Practical Playbook
Performance Engineering for AI Systems
Performance is no longer just a test at the end. For AI systems, it is an engineering discipline tied directly to quality, cost, reliability, and trust.
What to do
- Test the architecture, not only the prompt.
- Report p50, p95, p99, throughput, queue depth, timeout rate, retry rate, cache hit rate, provider errors, cost per successful task, and quality per second.
- Treat performance regressions as quality regressions when they change user trust, safety, business value, or release risk.
Evidence to preserve
- Report p50, p95, p99, throughput, queue depth, timeout rate, retry rate, cache hit rate, provider errors, cost per successful task, and quality per second.
- Preserve the inputs, versions, configurations, raw outcomes, and results for latency, performance engineering needed to reproduce work on Performance Engineering for AI Systems.
- Report results for latency, performance engineering by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Performance engineering for AI systems should instrument every span: prompt construction, retrieval, reranking, model call, tool call, judge pass, retry, cache lookup, fallback, streaming, and final rendering. Report p50, p95, p99, throughput, queue depth, timeout rate, retry rate, cache hit rate, provider errors, cost per successful task, and quality per second. Treat performance regressions as quality regressions when they change user trust, safety, business value, or release risk.
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Cite this page
Jason Arbon. "Performance Engineering for AI Systems." Testing AI Knowledge Edition, section 171.
https://jarbon.ai/testing-ai/knowledge/ch171-performance-engineering.html