Productivity
Pricing Tier Table and Messaging Builder
Pricing Tier Table and Messaging Builder turns goals and constraints into a structured, actionable response with clear constraints and an explicit output.
The prompt
Pricing Tier Table and Messaging Builder guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when planning work, making decisions, or organizing complex information and you want a response that is easier to evaluate and act on.
text prompt
You are a product marketing manager.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Design pricing tiers for {{product}} targeting {{customerSegments}}. Propose tier names, who each tier is for, what is included, and how to message the value. Include a tier comparison table and positioning notes.
Inputs:
- Product description: {{product}}
- Target customer segments: {{customerSegments}}
- Key competitors and price anchors: {{competitiveContext}}
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.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Pricing Tier Table and Messaging Builder",
"deliverable_type": "Pricing tiers and messaging",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Tier differences are clear and defensible",
"Messaging matches the target segment pains and jobs",
"Includes a comparison table that is easy to paste into a site",
"Calls out risks like cannibalization and packaging pitfalls"
],
"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 prompt1577 characters
You are a product marketing manager.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Design pricing tiers for a collaborative project-planning app targeting a specific customer segments. Propose tier names, who each tier is for, what is included, and how to message the value. Include a tier comparison table and positioning notes.
Inputs:
- Product description: a collaborative project-planning app
- Target customer segments: a specific customer segments
- Key competitors and price anchors: 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.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Pricing Tier Table and Messaging Builder",
"deliverable_type": "Pricing tiers and messaging",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Tier differences are clear and defensible",
"Messaging matches the target segment pains and jobs",
"Includes a comparison table that is easy to paste into a site",
"Calls out risks like cannibalization and packaging pitfalls"
],
"next_steps": []
}
Useful structure, room to move.
Pricing Tier Table and Messaging Builder guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when planning work, making decisions, or organizing complex information 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.
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