GPT Tasks/Develop a High-Ticket Offer
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Develop a High-Ticket Offer

Turn a real client problem, delivery capability, proof, resources, and constraints into a premium offer concept with scope, outcomes, delivery model, proof requirements, boundaries, pricing hypotheses, sales questions, and next actions.

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01

Configure task

Setup
Describe who the offer is for, the problem or situation it addresses, the desired outcome, and any real evidence about the client context.
Add what you can actually deliver, verified expertise/proof, team/resources, delivery process, capacity, dependencies, and constraints.
Prompt style
Additional options
Add current prices or ranges if known, scope limits, exclusions, add-ons, timeline/capacity limits, margin considerations, approval/legal boundaries, and claims that must stay conditional.

What you’ll need

  • Ideal client/problem plus the capabilities, proof, delivery resources, and constraints you can actually support
  • Ideal client, problem, and desired outcome
  • Capabilities, proof, and delivery reality

What you’ll get

  • Ideal-fit and non-fit criteria
  • Offer outcome and value logic
  • Scope and deliverables
  • Delivery model and responsibilities
  • Boundaries, exclusions, and dependencies
02

Prompt preview

Editable

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Task referenceExamples, expected output, boundaries, and routing.

Examples

1. Premium consulting project

Context: A consultant supplies a well-defined client problem, verified experience, prior proof, a realistic delivery process, capacity limits, and current project ranges.

Goal: Shape a premium project offer without inventing outcomes.

Useful output: Fit criteria, scope, delivery model, proof needs, pricing hypothesis, discovery questions, boundaries, and validation steps.

2. Ongoing advisory retainer

Context: An advisor supplies actual expertise, the ongoing client decisions they support, availability, response-time limits, existing pricing context, and non-included services.

Goal: Design a reviewable retainer concept.

Useful output: Core promise boundaries, responsibilities, cadence, exclusions, proof, pricing hypotheses, questions, and risks.

3. Structured service program

Context: A small team supplies a repeatable service process, target client context, existing proof, delivery capacity, and optional add-ons.

Goal: Create a core program plus add-ons.

Useful output: Program outcome, modules/deliverables, add-ons, responsibilities, proof gaps, pricing/packaging hypotheses, and next actions.

Expected output

  • Ideal-fit and non-fit criteria
  • Offer outcome and value logic
  • Scope and deliverables
  • Delivery model and responsibilities
  • Boundaries, exclusions, and dependencies
  • Proof/evidence requirements
  • Pricing and packaging hypotheses
  • Sales/discovery questions
  • Risks and unsupported claims
  • Priority next actions

Truth boundary

Do not promise income, conversions or business results. Do not invent proof, scarcity, testimonials, legal terms or delivery capability. Pricing and positioning are planning suggestions, not guarantees or financial/legal advice.

Overlap guard: Owns the design of a premium offer package: fit, scope, delivery model, proof needs, boundaries, and commercial hypotheses. Use Startup Business Plan for the whole venture, Client Proposal/SOW for a client-specific scoped document, Pricing Strategy for systematic price-model testing, and Value Proposition for reusable message positioning.

Privacy and handoff

Runs in this browser. No upload by this page. Copy/download locally or explicitly open ChatGPT or Gemini. Source files are attached in the chosen AI service, not uploaded by Mycelgrid. Review the generated prompt and AI output before using or sharing it.