Principle
Own the task eval before shopping for a better model
Without your own test, a model leaderboard is somebody else's job description.
When it fits
- A team is comparing models because an AI workflow feels unreliable or slow.
When to avoid it
- Small suites can overfit current work; refresh them as the task distribution changes.
Why it matters
Build a small representative task suite with the quality, latency and cost measures that matter to your workflow. Run candidate models through the same harness and starting state. Use public benchmarks as context, not as a substitute for local evidence. Keep the suite so future model releases can be tested quickly instead of restarting the comparison from opinion.
An example
For SAP incident analysis, compare models on anonymized diagnostic cases with required evidence and false-cause penalties rather than generic coding scores.
Check your result
A model choice can be explained from task-level evidence relevant to your workflow.
Keep this limit in mind
- Small suites can overfit current work; refresh them as the task distribution changes.
Connected ideas
Useful withRe-benchmark AI when the model or task changes
Evidence and sources
Anthropic argues that task evals let teams compare model or system changes against stable requirements instead of relying on impressions.
A stable eval can become stale or saturated and requires maintenance.
Demystifying evals for AI agents · Evals for model upgrades and baselines