Product Manager
Interview & 1:1 Prep#PM#interview#casePM Interview Prep · Case + Judgment Two-Track
Split PM interview prep into product-sense + judgment tracks. Pre-draft 3 likely case questions + 3 reverse questions in 30 minutes.
Pain addressed · PM candidates default to 'grind 30 product-sense questions' — but they skip real judgment reps. Interviewers are asking 'how do you decide?', not 'design a feature'.
PM Interview Prep · Case + Judgment Two-Track
You are my PM interview prep partner. Applying for {pm_level} (Sr PM / Lead PM / Group PM). Build a case + judgment dual-track brief.
# Company + role
- Company + product + stage: {company_product_stage}
- Position + JD: {position_jd}
- Interviewer + role: {interviewer_info}
- Recent news (launches / pivots): {recent_news}
# My PM experience (2-3, one line each)
{my_pm_experience}
# Output four sections
## 1. The company's current product bet (120 words)
Infer one core bet from product + stage + news. e.g. new platform v2 = betting on retention not acquisition. Cite sources.
## 2. 3 likely case questions + pre-answers (150 words each)
- Q1 product sense ("design X feature") → user job → metric → MVP → trade-off
- Q2 metric debug ("DAU dropped 15%, how do you investigate") → hypothesis tree + which data first
- Q3 prioritization ("you disagree with engineering") → framework + a real example from your work
## 3. Your 3 judgment stories (80 words each)
Not product sense — real decisions, including one mis-bet.
- Situation / Decision (with why) / Outcome (with data) / Hindsight (would you change it?)
## 4. Your 3 reverse questions (30 words each)
- One about the product north star (their real metric)
- One about decision rights (what PMs decide vs what the founder decides)
- One about failure (their most recent product mis-bet)
# Boundaries
- Don't recite product sense frameworks — apply one to a specific example.
- Judgment stories MUST include one mis-bet. Perfect-PM stories don't land.Open directly on your favourite AI
One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V). Recommended platforms are ordered by content fit.
Workflow · 5 steps
- 1
T-24h: dump product context, recent news, JD, and interviewer LinkedIn into one block.
- 2
Run in Claude (strongest at PM judgment reasoning) or DeepSeek R1. Fill the five placeholders.
- 3
Run through the 3 case pre-answers yourself, swapping generic examples for the company's actual context. Push the model if too abstract.
- 4
Audit the 3 judgment stories — at least one must be a mis-bet. All-success = the interviewer disbelieves you.
- 5
T-1h: write the 3 case frameworks from memory and the 3 reverse questions on a card.
Verify before you ship
AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)
Why this prompt works
This prompt isn't a generic “write me a Interview & 1:1 Prep”. It folds in the concrete context a Product Manager actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.
When to use
Best moment: you know roughly that you need to do a Interview & 1:1 Prep, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.
Common pitfalls
- Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
- Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
- Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.
Variants & extensions
Want to use this card more aggressively? Three extensions:
- Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
- Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
- Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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