Playbook

Non-technical Workflows & Checklists for Experiments

An interactive, non-technical playbook that guides business teams through designing, running, validating, and handing off small AI experiments. Includes structured templates for hypothesis, acceptance criteria, data packaging, validation checks, risk mitigation, and a handoff checklist intended to produce reproducible, engineering-ready artifacts.

Interactive Tool

Non-technical AI Experiment Playbook

This playbook helps non-technical teams design, run, validate, and hand off small AI experiments safely. Fill the form to create a repeatable record of the experiment, then save it. Use the acceptance criteria and validation checklist to decide when to hand off to engineering.

Keep entries concise. If you need help with data handling or compliance, contact your internal data governance or engineering team before productionization.

One-line name describing the goal.
YYYY-MM-DD
What problem are you solving or what decision are you trying to improve? (1–3 sentences)
If we [change], then [expected outcome] because...
Be measurable and specific. Include thresholds or ranges that define success or failure.
Describe sample size, file formats, anonymization steps, and where to store packaged data (path or link).
Choose the approach that best fits this experiment.
Describe sample tests, evaluation metrics, acceptance checks, and who will review outputs.
Select checks you will perform on outputs.
Choose Yes when acceptance criteria are met and artifacts are packaged.
List artifacts (sample data, evaluation results, prompts, notebooks, test cases, notes, links).
List any privacy, compliance, security, or business risks and how you'll mitigate them.
Total time to run initial experiment.
Fill after experiment completes — what worked, what didn't.
1 = low confidence, 5 = high confidence
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