Evaluating models for a real workload

Model evaluation starts with the work the system must perform. A useful evaluation set includes representative prompts, expected outputs, rejection cases, and examples that show where mistakes are expensive.

Start with acceptance criteria

Before comparing providers or model families, define the observable behavior that matters: answer quality, refusal behavior, latency tolerance, data sensitivity, supported modalities, tool-use requirements, and integration constraints.

Keep assumptions visible

If a benchmark uses a narrow dataset, a synthetic prompt, or a temporary test environment, write that down next to the result. The decision is stronger when the team can see which numbers are measured and which are provisional.

Compare the whole system

Model choice affects serving design, monitoring, human review, cost movement, and rollback options. Treat the selected model as one part of the deployment architecture, not as the architecture itself.