PromptSharp › Daily briefs › Product Management › September 18, 2026

PromptSharp Product Brief · free web issue

Product Management prompt of the day

September 18, 2026 · for PMs, product ops, founders wearing the PM hat. One sharp, copy-paste prompt — free, every weekday.

Prioritization & RoadmapsFREE

RICE the roadmap: score competing bets and expose the shaky assumptions

Everything is P0 and stakeholders are loud. Score the contenders on a consistent frame and surface which inputs are guesses.

You are a product strategist running a transparent prioritization pass — a decision aid, not the decision.

Produce:

A) SCORING TABLE — each candidate scored on Reach, Impact, Confidence, and Effort (RICE) using only my inputs, with the resulting score and rank.

B) CONFIDENCE AUDIT — for each item, the single input I'm least sure about, since that's what could flip the ranking.

C) TIER READ — the natural break between 'do now', 'do next', and 'not yet', and why the line falls there.

D) STRATEGIC CHECK — the one item the pure score ranks low that might still matter for a strategic reason I named, kept visible for a human call.

Inputs: [CANDIDATE ITEMS] · [REACH / IMPACT / EFFORT ESTIMATES] · [CONFIDENCE NOTES] · [STRATEGIC PRIORITIES]

Rules: Do not invent reach or impact numbers — use my estimates and mark any I left blank. The score informs; it doesn't decide. Keep confidential roadmap data out of consumer AI tools. This ranks the options; the prioritization call stays yours. Verify anything uncertain against the source before relying on it.

Why it works — Prioritization arguments go in circles because the inputs are hidden opinions; a consistent RICE table plus a confidence audit that names the shakiest input per item makes the debate about the right thing — the assumptions — and the strategic-check row prevents a purely mechanical score from burying a bet that matters for reasons the formula can't see.

What changed for Product Management

Bayesian Optimization with Rich Auxiliary Information via LLMs

Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet many real-world optimization problems contain substantially richer information than function evaluations…

arxiv

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