AI Evals Lab
An interactive proof-of-thinking tool for exploring what kind of eval stack an AI workflow deserves, where benchmark design breaks down, and what I would actually ship first.
I like evals most when they stay attached to a real workflow. The interesting question is rarely whether a model can score well on a benchmark in the abstract. It is whether the benchmark helps a team understand what would fail in production, what is reversible, and where human review still matters.
The controls here are intentionally simple. Change the product surface, the stakes, the output variance, reversibility, review budget, and data freshness. The lab then shifts the eval stack, the benchmark posture, the failure focus, and the proof artifact I would want to make visible.
Interactive Lab
AI evals lab
A small deterministic tool for deciding what kind of eval stack an AI workflow actually deserves, what benchmark posture makes sense, and which proof artifact is worth shipping first.
Presets
Tune the workflow
Live benchmark map
Rubric-first eval stack
Start with a small golden set, a written rubric, and a visible review loop so the team can learn what good actually looks like.
Benchmark posture
Task-shaped scorecard
Measure whether the workflow completes the real job, not whether the model sounds smooth in a demo.
Proof artifact
Eval brief plus scorecard
Ship a one-page artifact that defines the task, shows the failure buckets, and makes the review standard reusable.
What to focus on
Write a clear rubric before you collect more samples.
Keep the context fixed long enough to learn what the model itself is doing.
Measure whether the system saves real operator time.
First moves
Write the task spec in plain English before touching the harness.
Start with the smallest set of cases that still reveals the bad surprises.
Keep reviewer overhead light so the team learns quickly.
Search angle
AI evals product builder shipping benchmark-shaped workflow tools