Skip to main content
Migrating a call is step one. /slm-agent:optimize is step two: making the ScaleDown model’s output match or beat the frontier model on your data.

​
The loop

  1. Collect your API key (read from the environment, never written to disk) and a small set of labeled samples.
  2. Create samples if you have none. It finds representative inputs in your project, agrees a label schema with you, records a baseline answer per sample, and validates the set.
  3. Run a reproducible eval (scaledown-eval/run_eval.py) with a metric that can’t be gamed.
  4. Iterate the prompt one scoped change at a time. It re-runs the current best twice in the same batch to separate real gains from API noise, and scores the whole task so a win on one class that regresses another is caught.

​
Metric per task

​
Output

Everything lives under scaledown-eval/: the samples, each prompt version, per-round metrics, and an optimization-report.md with the winning prompt and the exact command to reproduce it. Sample data is only sent to the ScaleDown endpoint you provided a key for.