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俾Marketer用嘅AI Builder Reality Check
判斷你係咪ready build第一個真係有用嘅AI product slice，同埋如果而家開始，最先會爛嘅位係邊度。
模板欄位
- User and painful job
- First useful slice
- Proof that someone wants it
- Current artifact or prototype
- Weakest assumption
- Data or workflow dependency
- Review standard
- Launch risk
- Next 14-day build commitment
填寫示例
下面填寫示例會保留英文，因為Prova會用英文review提交嘅artifact。
A marketer trying to build an internal campaign QA assistant.
弱版本 vs 強版本
弱版本
| User | Marketing team |
|---|---|
| First useful slice | AI assistant for campaign QA |
| Proof | Everyone says QA is annoying |
| Weakest assumption | Need better prompts |
| Launch risk | Bugs |
| 14-day commitment | Build MVP |
點解唔夠好
- "Marketing team" is not a user.
- "AI assistant" is too broad.
- The proof is hearsay, not behavior.
- The weakest assumption ignores source data, approval rules, and workflow ownership.
- "Build MVP" is not a commitment.
強版本
| User | Paid media manager checking campaign launch settings before client approval |
|---|---|
| First useful slice | A checklist reviewer that compares campaign setup notes against required client launch rules |
| Proof | Two managers already use a manual spreadsheet before every launch; mistakes still reach strategist review |
| Current artifact | One sample launch checklist, three anonymized campaign setup notes, and one failed QA example |
| Weakest assumption | The checklist rules are explicit enough for AI to evaluate without platform access |
| Data/workflow dependency | Client launch rules, platform screenshots, naming conventions, approval owner |
| Review standard | Flag missing budget, geo, naming, tracking, claim, and approval evidence; never approve launch automatically |
| Launch risk | False confidence before a client-visible campaign goes live |
| 14-day commitment | Build a reviewer for one client and one platform, then test against five past launch packets |
點解有效
- The user is specific.
- The first slice is constrained.
- Proof comes from an existing workflow.
- Launch risk is clear.
- The commitment creates evidence, not just more code.
Prova會審視泛用AI成日漏咗嘅位
- Whether the first useful slice is still too large
- Whether the user is real or imagined
- Whether there is proof beyond personal excitement
- Whether the artifact standard exists before the build starts
- Whether the hard part is product judgment, data access, approval, or implementation
- Whether the next step should be reality check, build brief, build plan, execution lane, or launch gate
下一步
想攞feedback？Prova會先做一個短assessment，令review標準配合你嘅角色、目標同第一批受眾。之後你會入去適合而家工作嘅sprint。
Prova暫時只提供英文版。
提交前：請移除客戶名、保密數字，同任何team唔想存入training或coaching system嘅資料。
前往英文版Prova