Turn any AI into a senior product mentor — with one paste.
Generic answers become the kind of advice a seasoned product leader gives: a clear recommendation, the right framework for the moment, and a gut-check on your blind spots. This guide makes the whole skill visual — 30+ frameworks across 10 disciplines, the way a practitioner actually uses them.
See the difference — same AI, same question
Plain AI
“It depends on impact and effort. You could try a framework like RICE or MoSCoW. Gather stakeholder input, weigh each feature against your goals, balance quick wins against strategic bets…”
Lists options. Hands the work back to you. No recommendation, no opinion.
AI + product-skill
Use RICE here — you have rough reach and effort, so it gives a defensible ranking fast.
Ship A and C this quarter. A is high-reach, low-effort. C is the enabler the rest depends on. Defer B (high effort, unproven), park D + E.
⚠ Blind-spot check: RICE rewards this quarter's reach — if B is your enterprise bet, it'll quietly under-rank it. Flag it as a strategic call, separate from the score.
The A–E features are a placeholder to show the format — not a real case.
The operating loop
For any product task, it runs the same five moves.
Not a menu dump — a disciplined sequence that ends in a decision you can defend.
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01
Clarify only if ambiguous
Ask up to five sharp questions when the request is genuinely under-specified — otherwise proceed. No interrogation.
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02
Pick the one right framework
Match the method to the decision — don't dump the whole toolbox on the table.
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03
Output a decision-ready artifact
Recommendation first, reasoning second. A scoring table, an eval plan, a PRD — not an essay.
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04
Tag confidence
Every substantive call carries a label — from VERY HIGH to SPECULATIVE — so you know how hard to lean on it.
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05
Run the blind-spot check
Full ritual for high-stakes, irreversible calls; a one-line tag otherwise. Drop the ritual, never the thought.
Then the next task runs the same five moves — the loop, not a checklist.
The router
What are you trying to do right now?
Tap a task — the skill routes it to the one discipline that owns the answer, then hands you the page.
The task router needs JavaScript to run. Or jump straight in from the Field Guide — every discipline is one hop away.
The ten disciplines
The whole craft, in ten moves.
Each links to its page in the Field Guide — the frameworks, the traps, and the live tools for that discipline.
Strategy
01A focused sequence of bets — not a feature list.
Discovery
02Knowing what to build is the constraint.
Prioritization
03Match the method to the decision.
Metrics
04Pick one metric; ship its guardrail.
AI-Native PM
05Prove it works before you ship.
GTM
06Position deliberately; launch to de-risk.
Pricing
07Price is a product decision.
Growth & PLG
08Build loops, not funnels.
Org & Roadmap
09Empowered teams, not feature teams.
Career
10Bet on judgment, not headcount.
Start here
Twelve moves that carry most of the judgment.
If you read nothing else, read these. The canon the skill reaches for first — each links straight to where it lives.
RICE + kill rule
Reach × Impact × Confidence ÷ Effort. Confidence is an honest discount — kill anything under 50% rather than rank it.
Prioritization →JTBD job story
“When I [situation], I want to [motivation], so I can [outcome].” Frame the struggle, not the feature.
Discovery →Opportunity Solution Tree
Outcome → opportunities → solutions → assumption tests. Every solution ladders back to a real outcome.
Discovery →DHM moat
Delight · Hard-to-copy · Margin-enhancing — the three tests a durable strategic bet has to pass (Biddle).
Strategy →North Star + guardrail
One value-delivery metric + 3–5 movable inputs, paired with a guardrail that must not degrade.
Metrics →AARRR
Acquisition, Activation, Retention, Revenue, Referral — trace a problem upstream to where it really starts.
Metrics →Dunford 5-step positioning
Competitive alternatives → unique attributes → value → target market → market category, derived in order.
GTM →Price before product / WTP
Have the willingness-to-pay conversation during design. No WTP evidence, no spec (Ramanujam).
Pricing →Evals-as-QA loop
For any LLM feature the eval suite is the spec. Analyze → measure → improve, and re-run every change.
AI-Native →Minimum Viable Quality
Set a per-feature bar in three tiers — do-not-ship · acceptable · delight — with a cost envelope up front (Nika).
AI-Native →Type-1 / Type-2 reversibility
One-way doors need pre-mortems and staged approvals; two-way doors just need a fast, reversible call.
Heuristics →Playing-to-Win cascade
Five linked choices: winning aspiration → where to play → how to win → capabilities → management systems.
Strategy →Explore the guide
Six ways in.
Use it in 60 seconds
Paste it into the AI you already use — ChatGPT, Claude, Gemini or Perplexity — and ask your product question normally.
Get started → Field Guide10 disciplines, made visual
The whole craft in browsable depth — frameworks, anti-patterns, and the calibration that keeps advice in the right sector.
Browse → PROCurated worked depth
Seven modules — pricing, evals, positioning, growth, discovery, metrics, strategy — with the numbers worked through.
Go deeper → TemplatesCopy-ready skeletons
The 10-section PRD, the AI-codegen PRD, a strategy doc and an OKR fill-in — lead with the decision, not the structure.
Copy one → Tools13 live calculators
Score, sequence, price and pressure-test in the browser — RICE, WSJF, EVC, positioning, blind-spot and more.
Run them → ReferenceThe A–Z index
Every framework, one line each, filterable by discipline — plus ⌘K search from any page.
Look it up →