The prompt

Growth Experiments guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when exploring data and explaining what the results support and you want a response that is easier to evaluate and act on.

text prompt
You are a growth marketing lead. Task: Build an experimentation backlog of 25 tests for {{product}}. Rank by expected impact, effort, dependencies. Context: {{productBusinessContext}}. Inputs: - Product: {{product}} - Paste product/business context: {{productBusinessContext}} Output requirements: - Return JSON only. Do not wrap in Markdown or code fences. - Do not add keys beyond the schema. - Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable using assumptions. - Add a short, realistic next_steps list that would be performed after using this deliverable. - Include deeper reasoning artifacts as structured fields (assumptions, risks, tradeoffs) while keeping the final deliverable usable. JSON schema (example values only, keep the same keys): { "title": "25 Growth Experiments", "deliverable_type": "Prioritized experiment table", "clarifying_questions": [], "assumptions": [], "deliverable_markdown": "", "quality_checks": [ "Messaging aligns to the target audience and offer", "Includes clear success metrics and tracking", "Recommendations are actionable with next steps" ], "next_steps": [] }

Tune the prompt, not the plumbing.

Every control comes from this prompt’s content schema. Changes stay in your browser and update instantly.

Customized prompt1280 characters
You are a growth marketing lead. Task: Build an experimentation backlog of 25 tests for a collaborative project-planning app. Rank by expected impact, effort, dependencies. Context: Paste the relevant source material and context here.. Inputs: - Product: a collaborative project-planning app - Paste product/business context: Paste the relevant source material and context here. Output requirements: - Return JSON only. Do not wrap in Markdown or code fences. - Do not add keys beyond the schema. - Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable using assumptions. - Add a short, realistic next_steps list that would be performed after using this deliverable. - Include deeper reasoning artifacts as structured fields (assumptions, risks, tradeoffs) while keeping the final deliverable usable. JSON schema (example values only, keep the same keys): { "title": "25 Growth Experiments", "deliverable_type": "Prioritized experiment table", "clarifying_questions": [], "assumptions": [], "deliverable_markdown": "", "quality_checks": [ "Messaging aligns to the target audience and offer", "Includes clear success metrics and tracking", "Recommendations are actionable with next steps" ], "next_steps": [] }

Useful structure, room to move.

Growth Experiments guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when exploring data and explaining what the results support and you want a response that is easier to evaluate and act on.

The prompt establishes the job first, then supplies concrete decisions a model can act on. The variables preserve that structure while letting you change the subject, context, or output.