Building Your Preparation Plan

Four weeks is the right preparation window for most candidates. Less than that and you are relying on talent over preparation, which is a gamble. More than that and you start over-preparing on details that rarely come up while losing the freshness that comes from having recently thought about problems in new ways. Here is how to allocate the time.

Week 1: Foundation. Spend this week building AI fluency. Read 'Designing Machine Learning Systems' by Chip Huyen. Study 10 AI products in depth: how they work technically, what their evaluation metrics are, and what their failure modes look like. Start a one-page 'AI PM cheat sheet' with the terms and frameworks you will reference in interviews.

Week 2: Product Sense and Technical. Practice 2 product sense questions and 2 technical questions per day. Use the AIDE and BEAM frameworks from Modules 2 and 4 of this course. Record yourself answering and listen back. You will cringe, and that is the point. The gap between how good your answer sounds in your head and how it sounds out loud is the gap you need to close.

Week 3: Strategy and Behavioral. Practice 2 strategy questions and prepare 5 behavioral stories using the STAR-AI framework from Module 5. Each story should be ready to deliver in 2 to 3 minutes and should highlight AI-relevant skills even if the original experience was in traditional PM work. Run at least 2 mock interviews with friends or peers who are PMs.

Week 4: Full Mocks and Polish. Run 2 to 3 full mock interview loops (4 rounds each). Identify your weakest question type and do targeted practice on that type. Review your 'AI PM cheat sheet' daily. Practice your 'tell me about yourself' narrative until it takes exactly 90 seconds and covers: your PM background, your transition to AI, and what makes you specifically qualified for this role.

This plan assumes you are preparing while working full-time. If you have dedicated preparation time, compress to 2 weeks. If you are interviewing at multiple companies simultaneously, extend to 6 weeks but add company-specific preparation in weeks 5 and 6.

Generic preparation gets you to a 3 out of 5. Company-specific preparation gets you to a 4. The difference is significant because most candidates are in the 3 to 3.5 range, and the bar for hiring is typically 3.5.

For Google: Practice product sense questions obsessively. Google interviewers expect you to generate 3 to 4 user segments, pick one with a clear rationale, and design a complete product in 35 minutes. Study Google's AI products (Gemini, Search Generative Experience, Cloud AI) and have opinions about what they should build next. Know Google's AI principles by name.

For Meta: Focus on execution scenarios. Meta values 'move fast' even in AI. Practice questions about shipping AI features under tight timelines and making scope tradeoffs. Study Meta's AI products (recommendation systems, content moderation AI, Meta AI assistant). For Anthropic and OpenAI: Go deep on technical fluency and AI safety. Read their published research, understand RLHF and constitutional AI, and be prepared to discuss responsible deployment of powerful AI systems.

Create a simple spreadsheet with four columns: Question Type, Date Practiced, Self-Score (1-5), and Notes. After each practice session, log the question you practiced and score yourself honestly. You should see your average self-score increase from roughly 2.5 in week 1 to 3.5+ by week 4. If a question type is not improving, allocate more time to it.

The most important metric is not your average score. It is your floor. Interviewers rarely reject a candidate for a single mediocre answer. They reject candidates who have one answer that falls below the bar. Your goal is to raise your floor to a 3 on every question type, then raise your ceiling on your strongest types. A candidate who scores 3, 3, 4, 4 across four rounds beats a candidate who scores 5, 5, 2, 2 every time.