ML OpsNov 10, 2024• 3 min• ArcTrait Engineering
Evaluation loops that don’t slow you down
A lightweight eval loop for AI teams that need signal without ceremony.
We like evaluation that fits inside the sprint, not outside of it.
- Curate 20 edge cases. They should be ugly: long text, noisy inputs, and tricky exceptions. We keep them in version control so they travel with the code.
- Write tiny assertions. We store expectations alongside the sample so we can see what broke. For LLM outputs, this could be as simple as a regex match or a minimal score threshold.
- Automate the run. A single script runs the cases locally and in CI. Here’s a sketch:
import { evaluate } from './lib/evaluator' import { samples } from './fixtures/samples' async function main() { const results = await Promise.all(samples.map((sample) => evaluate(sample))) const failures = results.filter((r) => !r.passed) if (failures.