ModelRefs / Evaluation Pipeline — Canonical Workflow
Evaluation Pipeline — Canonical Workflow
Evaluation Pipeline: provisional AI workflow implementation reference with candidate models, providers, tools, and architecture.
What this reference supports
An evaluation pipeline is a continuous offline/online harness for testing model, prompt, or system changes against a baseline — regression detection, dataset versioning, and dashboards so quality, cost, or latency regressions surface before reaching production.
Use this page to check what an evaluation harness needs to cover for your workload — baseline comparison, dataset versioning, and regression thresholds — then review the related architecture, tool, and benchmark references before implementing one.
This workflow's representative-workload evaluation protocol has been drafted, but ModelRefs does not yet have recorded run evidence against it. Treat readiness or completion claims as provisional and evaluate the harness on your own representative workload before relying on it for production gating.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Evaluation Pipeline — Canonical Workflow.