An eval is a repeatable way to score AI outputs against criteria you care about. Traditional tests tell you the code works; evals tell you whether the AI output was any good. They combine a quality rubric, a golden dataset of test inputs, an eval pipeline, and explicit pass thresholds.

What does the AI Eval Builder produce?

A complete eval plan you can copy or download as Markdown, plus a starter golden dataset that exports as CSV or JSON. The plan covers your quality rubric with weights and hard-fail criteria, dataset categories, automated checks, LLM-as-judge configuration, human review cadence, and tradeoff boundaries.

Who is the AI Eval Builder for?

Product managers shipping AI features who need to define what good looks like before launch. It speaks plain language by default, with expandable notes for engineers. It takes about 15 minutes.

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Lab Notebook · Experiment Nº 001

AI Eval Builder

Shipping an AI feature without evals is shipping on vibes. Build a real eval for your feature: a quality rubric, a starter golden dataset, and a pipeline your team can run with. Free, no signup, runs in your browser.

A quality rubric

The dimensions that matter, weighted, with hard-fail criteria

A golden dataset

30 to 50 test cases across happy path, edge, and adversarial

An eval pipeline

Automated checks, LLM-as-judge, and human review cadence

Pass thresholds

A bar derived from stakes and reversibility, with the math shown

5 steps · about 15 minutes

Build your eval plan

You'll walk out with a quality rubric, a starter golden dataset, an eval pipeline recommendation, and a complete plan you can hand to your team.

1. Describe your feature

What it does, what good and bad look like, what a failure costs you

2. Quality rubric

AI drafts scoring dimensions from your description; you edit anchors, weights, and hard-fails

3. Golden dataset

AI generates starter cases across happy path, edge, and adversarial; you curate

4. Eval method

Automated checks, LLM-as-judge setup, human review cadence, tradeoff boundaries

Your work saves in your browser as you go. What you type in Step 1 is sent to an AI model only to draft your rubric, dataset, and method, and is not stored server-side.

Want the why? How evals work

Optional background. The tool above stands on its own.

Want help making this real?

A plan is the easy part. I help teams stand up eval pipelines that actually run, and keep running after I leave.

Work with me