The task
You’re an indie dev or small-team PM. Your backlog has 30+ feature ideas. You have one quarter and at most three to ship well. You need AI to do the brutal cut.
When this is the right job for AI
- You can hand AI the user signal behind each feature — not just titles.
- You have a north-star outcome for the quarter (“retention week-4 from 22% to 30%”).
- You will live with the cut. AI is brutal in the prompt and softer in the output; the cut is yours.
What to feed the AI
- 20-30 backlog items: name + the user signal (review quote, support ticket count, churn cohort evidence)
- North-star outcome for the quarter
- Capacity: how many “shippable feature weeks” you actually have after on-call, bugs, App Review delays
- 2 features your gut already wants to drop
- 2 features competitors just shipped that you are tempted to chase
Copy-ready prompt
You are cutting a feature backlog from 25 items to 3 for the next quarter.
North-star outcome: week-4 retention from 22% → 30%.
Capacity: 10 shippable feature weeks (13-week quarter, -2 for on-call, -1 for App Review delays).
Backlog (name | user signal):
1. Streak freeze | 38 support tickets, 12 reviews mention "lost my streak"
2. Widgets v2 | 9 reviews, gut says it’s cosmetic
3. Reminders by time-of-day | 1 review, 6 support tickets — power-user request
4. Apple Watch app | competitor just shipped, no user signal
5. Shareable progress card | 22 organic-share screenshots on X, no requests
6. Onboarding rewrite | analytics: 41% drop in step 3 of 4
7. Premium price test ($4.99 → $6.99) | revenue model assumption
8. Dark mode v2 | 14 reviews, mostly "the contrast hurts"
9. iPad layout | analytics: 8% of MAU on iPad, current layout broken
10. Settings cleanup | engineering ask, no user signal
... [paste 15 more]
Gut wants to drop: Apple Watch app, settings cleanup.
Tempted to chase: competitor widgets, competitor watch app.
Output:
1. The 3 features I ship this quarter. For each: which retention lever it pulls, weeks of effort, the one risk.
2. The 5 "no" features with one-line reasoning (specifically why NOT, not "lower priority").
3. The one feature I should explicitly *not* chase from competitor moves, and the 1-line rebuttal to my own FOMO.
4. The non-feature work I am underestimating: bugs, perf, onboarding-fix that will move retention more than any feature.
5. What I should be measuring weekly to know if the 3 commits are working.Sample output structure
Ship this quarter:
- Onboarding rewrite (3 weeks) — direct lever: 41% drop at step 3 = lost half the retention cohort before week 1. Risk: rewriting onboarding while changing pricing confounds the measurement.
- Streak freeze (2 weeks) — 12 reviews + 38 support tickets is the loudest single signal. Risk: streak-haters will complain freezes “cheapen” the mechanic; have a one-tap off.
- Reminders by time-of-day (2 weeks) — power-user retention proxy; the support-ticket cohort retains 1.7x. Risk: notification UX needs care or you push users out.
Not this quarter (5 nos):
- Apple Watch app: chasing a competitor with zero user signal. No.
- Settings cleanup: zero user-visible retention impact.
- Widgets v2: cosmetic; widgets v1 already meets the bar.
- Dark mode v2: real complaint but doesn’t move retention week-4.
- iPad layout: 8% of MAU, important — but next quarter, not this one.
Don’t chase: Apple Watch. Rebuttal to your own FOMO: “watch apps add 40% maintenance load, your audience uses watches at 12%, and your competitor’s ship is a marketing bet, not a retention bet.”
Underestimated non-feature work: the 6 P1 bugs in your TestFlight. Each one costs you a 4-week-retention point in cohorts. Allocate 1 week.
Weekly measurement:
- Onboarding funnel step 3 conversion (weekly).
- Streak-related ticket count (weekly).
- Week-4 cohort retention (rolling 6-week chart).
How to refine
- Output too generic → require “each ship-feature names a specific retention mechanism, not
improves engagement.” - AI scores everything equally → demand “the 3 features must include exactly one onboarding/funnel fix, not three power-user features.”
- AI hedges → strict rule: “no
consider. Either ship or no.” - Skips the underestimated work → ask explicitly for “what’s missing from the backlog that is more important than any feature on it.”
Common mistakes
- Equating “most requested” with “ships first.” Most-requested features often serve power users who already retain.
- Letting competitor moves overweight your roadmap. Their bet isn’t your bet.
- Not budgeting non-feature work. Bugs and onboarding fixes often dominate retention math.
- Picking 3 features that all need the same engineer. Capacity is per-engineer too.
Practical depth notes
For AI Feature Prioritization for Indie Apps: From 30-Item Backlog to a 3-Item Quarter, the difference between a usable AI result and a generic one is the input packet. Give the model the audience, the current draft or raw material, the desired format, the decision you need to make, and two examples of what good and bad output look like. Ask it to preserve facts first, then improve structure or wording second.
After the first response, do a separate review pass. Look for missing constraints, invented details, weak calls to action, and language that sounds plausible but does not match the real situation. The best final output should be easy to use immediately: clear owner, clear next step, and no hidden assumption that someone else has to untangle.
FAQ
- Should I include “tech debt” in the 3? Only if it’s blocking a feature on the ship list. Otherwise it goes in the 1 week of underestimated work.
- What if my north star isn’t retention? Same template — swap “retention” for “activation” or “revenue per user.” The “what lever does this pull” question is the same.
- Can AI replace user research? No. Feed it user signal; don’t ask it to invent signal.
Related
- AI roadmap planning
- AI sprint planning
- AI user feedback clustering
- AI negative review analysis
- Feature Prioritization Prompts: RICE, MoSCoW, Kano
Tags: #AI writing #Feature priority #Product #Roadmap #Prioritization