Section 193 · Chapter 8, Operating AI: Observability, Relevance, and Economics
Modern EvalOps and AI Quality Platforms
Modern AI evaluation is a lifecycle: datasets, tasks, scorers, experiments, traces, online monitors, human review, and release gates feeding each other.
What to do
- Start with a spreadsheet if that is what gets the team moving.
- Choose tools that make quality evidence hard to lose, easy to rerun, and credible enough to support a decision.
- Version datasets, scorer code, judge prompts, rubrics, model routes, tool schemas, retrieval snapshots, random seeds, and environment state.
- Record cost and latency per case.
- Keep immutable run records.
Evidence to preserve
- Record cost and latency per case.
- Track scorer drift and human-review overturn rates.
- Preserve the inputs, versions, configurations, raw outcomes, and results for release gate, human review, trace, EvalOps needed to reproduce work on Modern EvalOps and AI Quality Platforms.
- Report results for release gate, human review, trace, EvalOps by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, treat EvalOps like software delivery infrastructure. Version datasets, scorer code, judge prompts, rubrics, model routes, tool schemas, retrieval snapshots, random seeds, and environment state. Record cost and latency per case. Keep immutable run records. Separate offline evals from online monitors. Track scorer drift and human-review overturn rates. Promote production failures into regression suites with ownership and expiry rules.
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Cite this page
Jason Arbon. "Modern EvalOps and AI Quality Platforms." Testing AI Knowledge Edition, section 193.
https://jarbon.ai/testing-ai/knowledge/ch193-modern-evalops-quality-platforms.html