Prompt Details
Model
Chat - (gpt-5.6)
Token size
1,371
Example input
[PRODUCT_IDEA]: A privacy-friendly Windows desktop app that automatically organizes the Downloads folder into sensible folders and lets the user undo every move.
[TARGET_CUSTOMER]: Remote office workers who download many PDFs, images, spreadsheets, and attachments each day.
[PROBLEM_AND_ALTERNATIVES]: Problem/job: Their Downloads folder becomes chaotic, and finding or filing documents takes repeated manual effort. Current alternatives: Manual sorting, Windows search, browser download history, or leaving the folder messy.
[EVIDENCE_AND_CONSTRAINTS]: Known evidence: Three coworkers independently complained about the problem. Two said they clean the folder every Friday. Nobody has agreed to pay or test a solution. Constraints: One solo creator, two weekends, a maximum cash budget of $100, Windows only, access to ten office workers, and no finished software.
[OUTPUT_LANGUAGE]: English
Example output
# 48-Hour Demand Test
## 1. Direct diagnosis
**TEST NOW — confidence MEDIUM.** Repeated complaints and an existing weekly cleanup behavior indicate a problem worth testing, but there is no evidence that users will trust automation or pay for it.
## 2. Neutral idea statement
For remote office workers whose Downloads folders become difficult to navigate, the idea offers reversible automatic organization instead of manual sorting, search, or tolerating the mess.
## 3. Evidence ledger
| Claim | Current support | Status | What would prove or disprove it |
| ------------------------------------------------ | -------------------------- | ---------- | ------------------------------------------------------------- |
| Folder disorder is recurring | Two people clean weekly | EVIDENCE | Observe frequency and minutes spent across 8–10 users |
| The problem is painful enough to change behavior | Three complaints | SIGNAL | Users share a real folder sample and try a proposed workflow |
| Automatic sorting is trusted | None | UNKNOWN | Users approve a preview and allow a reversible manual pilot |
| The outcome is better than search/manual sorting | Alternatives are described | ASSUMPTION | Compare time and confidence before and after a simulated sort |
| Users will pay | None | UNKNOWN | Obtain a paid-pilot commitment at a stated price |
## 4. Evidence scorecard
| Dimension | Score 0–5 | Confidence | Reason |
| --------------------- | --------: | ---------- | ----------------------------------------------------------- |
| Pain | 2 | MEDIUM | Complaints exist, but impact is not measured |
| Frequency | 3 | MEDIUM | Two users report a weekly cleanup habit |
| Existing effort/spend | 2 | MEDIUM | Manual effort exists; no spending is known |
| Reachability | 5 | HIGH | Ten relevant workers are accessible |
| Differentiation | 2 | LOW | Reversibility may help, but superiority is untested |
| Delivery feasibility | 3 | MEDIUM | A Windows prototype appears compatible with the constraints |
**TOTAL EVIDENCE STRENGTH: 17/30. This is not a success probability.**
## 5. Riskiest assumption
At least two of ten target users will trust a reversible organizer enough to submit a real folder sample and commit to a $15 paid pilot.
* Trust is essential because the product moves personal files.
* Complaints do not prove willingness to grant access.
* Without meaningful behavior, building classification logic is premature.
## 6. Current alternative map
* Supplied: manual sorting
* Supplied: Windows search and browser history
* Supplied: leaving the folder disorganized
* Inferred category: simple rule-based folder automation
## 7. The 48-hour experiment
* **Hypothesis:** At least five of ten workers will provide real workflow evidence, three will request a pilot, and two will accept a $15 paid pilot.
* **Target participants:** Ten remote office workers who download at least 20 work files per week.
* **Smallest test asset:** A one-page mockup showing before, proposed folder groups, an approval screen, and an Undo All action.
* **Recruitment channel:** Direct messages to the ten accessible workers.
* **Requested behavior:** Share a redacted screenshot or filename list, review a manually prepared organization preview, and accept or decline a $15 pilot.
* **Outreach message:** “I’m testing a privacy-friendly Windows tool for messy Downloads folders. I’m not asking you to install anything. If you download 20+ work files weekly, would you share a redacted screenshot or filename list? I’ll return a proposed folder layout and show exactly what would move. If it saves time, I’ll ask whether you would join a $15 reversible pilot.”
* **Five interview questions:** 1. Walk me through the last time you could not find a downloaded file. 2. How often do you clean the folder? 3. What do you do with sensitive filenames? 4. What would make automated moving unacceptable? 5. What did you expect the proposed layout to do differently?
* **Pass / revise / stop thresholds:** PASS: 5+ provide workflow evidence, 3+ request the pilot, and 2+ accept $15. REVISE: 3–4 provide evidence but fewer than 2 accept; revisit trust or pricing. STOP: fewer than 3 provide evidence or nobody wants a pilot.
* **Time and cash cap:** 6 hours and $0–20.
## 8. What not to build yet
* Cloud synchronization
* AI semantic search
* Multi-device support
* Permanent background monitoring
* Custom rule marketplace
## 9. Next step if the test passes
Within seven days, create a local prototype that only previews proposed moves and requires confirmation before changing files.
## 10. Final decision
**TEST NOW.** Change the decision only when real users either provide folder evidence and accept a paid pilot, or consistently refuse because the trust barrier is too high.
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GPT-5.6
Stop guessing whether a product idea has real demand. This evidence-first prompt identifies the riskiest commercial assumption and turns it into a practical 48-hour validation experiment—before you build. Get an evidence-vs-assumption audit, a six-factor scorecard, outreach copy, neutral interview questions, measurable pass/revise/stop thresholds, and one clear next step. Works for apps, services, physical products, and digital products.
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