You must compare two LLMs for a product. What is the best evidence?
Why D is the answer
A benchmark built from your own representative tasks
Public scores may not reflect your task, so test on your own work. Parameter counts and cards do not settle it either.
In detail
- Your own tasks are the fairest test.
- Build the benchmark from real work.
Use case
An illustrative scenario, not a real incident.
Two models tie on public scores, but one is far better on a team's real tickets.
See it in different ways
flowchart LR
A["Baseline"] --> B["Change one thing"]
B --> C["Run the same test set"]
C --> D{"Better?"}
D -->|Yes| E["New baseline"]
D -->|No| Amindmap
root((Data analysis))
Metrics
Loss
R-squared
Charts
Line: over time
Scatter: two variables
Care
Same data
Confounding factorsflowchart TD
Q{"Where does the score come from?"}
Q -->|"Your own tasks"| A0["Strong evidence"]
Q -->|"Parameter counts or model cards"| A1["Weak evidence"]Key point: Test on your own tasks.
Why the other options are wrong
- A. The published leaderboard rank for each of the models
- A public rank may not reflect your task.
- B. The number of parameters reported for each model
- Parameters do not measure quality.
- C. The model card that each of the vendors wrote
- A vendor's card is not a test on your data.