PRD to A/B Tests

Paste your PRD or feature brief. SPM turns it into a structured A/B test plan with a clear hypothesis, primary and guardrail metrics, variants, and a sample-size estimate. Stop shipping experiments you cannot read.

PRD or feature brief
Paste your content above

A hypothesis you can fail

Falsifiable by design, so the result is unambiguous either way.

Guardrails, not just a win metric

Catches harm to retention, latency, or revenue before it ships.

Feasibility before you build

A sample-size estimate that tells you if the test can even run.

How it works

  1. 1
    Paste your PRD or feature brief

    A full PRD, a feature spec, or a short description of the change you plan to ship. Markdown, plain text, or a paste from Notion or Google Docs all work.

  2. 2
    SPM designs the experiment

    The AI writes a falsifiable hypothesis, picks a primary metric tied to the feature goal, adds guardrail metrics that catch unintended harm, and defines control and treatment variants.

  3. 3
    Get a test plan you can hand to data

    A complete plan with hypothesis, metrics, variants, a sample-size and duration estimate, and the decision rule for what to ship. Ready to review with your data partner.

What you get

Falsifiable hypothesis

A clear "if we change X, then metric Y will move because Z" statement, not a vague goal.

Primary and guardrail metrics

One success metric tied to the feature goal, plus guardrails that catch harm to retention, latency, or revenue.

Control and treatment variants

Each variant defined precisely so engineering and data know exactly what is being compared.

Sample size and duration estimate

A rough sample-size and run-time estimate so you know if the test is even feasible before you build it.

FAQ

Is this free?

Yes. 2 free A/B test plans, no sign-up required. Sign in with Google for unlimited access and 30 expert document reviews.

What input works best?

A PRD or feature brief that states the change and its intended outcome. Even a short description of the feature works. The clearer the goal, the sharper the hypothesis.

Will the sample-size estimate be exact?

It is a directional estimate based on common baseline and effect-size assumptions. Treat it as a feasibility check, then confirm the exact number with your data team and your real baseline rates.

How is this different from asking ChatGPT for an A/B test?

ChatGPT gives a generic test outline. SPM writes a falsifiable hypothesis, separates primary from guardrail metrics, defines variants precisely, and states the decision rule, scored against an experiment-design framework.

Can SPM also review my full PRD?

Yes. SPM reviews your PRD against 30 expert standards before you design a single experiment. See all reviews →

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