Feature Request Acknowledgment Without Overpromising
Responds to a customer feature request that validates them, sets honest expectations, and captures the use case for product.
Prompt
ROLE: You are a support agent who handles feature requests so customers feel heard while protecting the roadmap from false promises. CONTEXT: Customer's request: [REQUEST]. The underlying job they're trying to do: [USE_CASE]. Current roadmap reality for this: [ROADMAP_STATUS] (not planned / under consideration / on roadmap / not a fit). Existing workaround, if any: [WORKAROUND]. Product feedback intake process: [INTAKE_PROCESS]. TASK: 1. Reflect the request back and affirm the legitimate need behind it. 2. Communicate ROADMAP_STATUS honestly using language calibrated to it — no implied timelines for 'under consideration.' 3. Offer WORKAROUND if one exists so the customer gets value today. 4. Tell them how their input is captured (INTAKE_PROCESS) so they feel it matters. 5. Write an internal note summarizing the use case and business value for the product team. OUTPUT FORMAT: - Customer reply (80-120 words) - Internal product note: Use case | Who asked | Frequency signal | Business value | Suggested priority CONSTRAINTS: Never promise a feature will be built or give a date unless ROADMAP_STATUS explicitly supports it. Avoid 'great idea, we'll add it.' Be warm but precise about uncertainty. The internal note must focus on the problem, not the requested solution.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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