A new model is released. How do you decide whether to adopt it?
Why A is the answer
Run your eval suite on it and compare quality, cost and latency
Only your own evals show how a model behaves on your task. Pin versions so that the change happens through a decision.
In detail
- Only your own evals show how a model behaves on your task.
- Compare quality, cost and latency.
- Pin versions, so that a change happens through a decision.
- Pin versions, so changes are a decision.
Use case
An illustrative scenario, not a real incident.
A new model launches. The team runs its eval suite and finds better quality but higher latency on one task, so it adopts the model everywhere else.
See it in different ways
flowchart LR A["Real labeled cases"] --> D["Eval suite"] B["Edge and adversarial cases"] --> D C["Human review samples"] --> D D --> E["Release decision"]
sequenceDiagram participant E as Eval suite participant T as Limited test participant P as Production E->>T: Promising version T->>T: Compare a real outcome T->>P: Roll out, or keep the pinned version
flowchart LR S0["Model released"] --> S1["Run eval suite"] --> S2["Compare quality, cost and latency"] --> S3["Decide"]
Key point: Adopt a model when your evals say so.
Why the other options are wrong
- B. Read the launch announcement and compare the published benchmark scores
- Published benchmarks do not reflect your prompts or data.
- C. Adopt it straight away, because newer models are always better in every respect
- Newer does not mean better for every task.
- D. Wait until a competitor has adopted it and reported the results publicly
- Someone else's results are not measured on your task.