Your 6-Week Prep Calendar

Six weeks is enough time to go from solid product instincts to genuine AI PM readiness — if you use the time deliberately. This calendar is structured around the eight competencies from Chapter 4, the company-specific signals from Chapter 5, and the practice architecture in Part 2. Each week has a primary focus, a set of practice prompts, and one specific interview signal to sharpen.

Before you begin Week 1, take your baseline assessment at outcomeos.online. The PM assessment is free, takes 15 minutes, and gives you a four-cluster breakdown of exactly where to focus. Do not skip this step. Starting prep without a baseline is like training for a race without knowing your current pace. The assessment will tell you which parts of this calendar to weight most heavily.

Primary reading: Chapters 1 through 3 of this guide. Your goal for this week is orientation and vocabulary. You are building the mental map of AI PM work: what it actually involves, what interviews actually test, and the technical concepts that will come up in every interview you have.

Practice prompts for this week: Explain the difference between RAG, fine-tuning, and prompting to a non-technical stakeholder. Describe a product feature you have used that you suspect has an AI component, and explain how you think it works. Walk through the trust collapse scenario from Chapter 1 and identify what you would have done differently at each decision point.

Interview signal to sharpen this week: precision in technical explanations. The goal is to explain AI concepts clearly and specifically, without either oversimplifying or overcomplicating. Practice talking out loud. Record yourself. Listen for hedging, filler, and imprecision. The ability to explain a technical concept cleanly in thirty seconds is a signal that lands in every interview.

After your outcomeos.online assessment, review your four-cluster results. Note your lowest cluster. That cluster gets a dedicated block of time each week for the rest of the calendar, in addition to that week’s primary focus.

Primary reading: Part 2, Product Sense section and Evals section. These are the two competencies where AI PM interviews diverge most sharply from traditional PM interviews.

Product sense in an AI context requires understanding how probabilistic behavior changes the user experience frame. Evals is a competency that barely exists in traditional PM prep and is tested heavily in AI loops.

Practice prompts for this week: Design an evaluation framework for an AI-powered meeting summarization feature used by enterprise teams. A user reports that the AI assistant in your product “gave them wrong information.” Walk through how you would triage and respond.

You are launching an AI feature to 5% of users in a limited beta — what metrics do you track in the first two weeks, and what thresholds would cause you to pause the rollout?

Interview signal to sharpen this week: the difference between capability evaluation and safety evaluation. Most candidates discuss evals in terms of accuracy. Strong candidates also discuss safety — what the feature should not do, how you would catch misuse, and what you would do if it happened. Practice making this distinction naturally in your eval answers.

Primary reading: Part 2, Cost/Latency/Routing section and Strategy section. This week covers the economic and competitive dimensions of AI PM work. Cost and latency questions appear in almost every serious loop and are consistently underprepared. Strategy questions at AI companies have different contours than at traditional software companies — the moat question, in particular, requires a different answer when model capabilities are commoditizing.

Practice prompts for this week: A new, cheaper model is available from your provider. It is 30% less accurate on your internal eval. Your current inference cost is $0.08 per call and you have 1.5 million daily active users. How do you think about whether to switch? Design a routing system for an AI product that uses three different models with different cost/quality tradeoffs. How would you build a durable competitive moat for an AI product when model capabilities are available to all competitors?

Interview signal to sharpen this week: building a cost model on the fly. Interviewers at several of the companies in Chapter 5 will ask you cost questions with real numbers and watch how you structure your thinking. Practice building simple token cost models out loud: calls per user per day, tokens per call, cost per token, monthly cost at scale. The math is not hard. The structure is what they are evaluating.

Primary reading: Part 2, Execution section and Estimation section. Execution in AI PM work requires different planning models than traditional software. Estimation in AI work involves variables — inference costs, accuracy improvement curves, token usage patterns — that do not appear in traditional PM estimation. Both competencies reward candidates who have thought about AI-specific failure modes in their planning.

Practice prompts for this week: Plan the rollout of an AI coding assistant for a B2B SaaS company. Include staged rollout design, success metrics, monitoring plan, and rollback criteria. Estimate the monthly inference cost of serving an AI answer feature to users of a search product with 10 million monthly active users and an average of 8 searches per session. A model update from your provider changed behavior in ways users are noticing but that do not show up as regressions on your eval suite. How do you diagnose and respond?

Interview signal to sharpen this week: structured rollout thinking. The ability to describe a staged AI feature rollout with specific gates, metrics, and rollback criteria is a signal that distinguishes candidates who have managed AI features in production from those who have only designed them in theory. Practice saying this out loud with a specific feature in mind.

Primary reading: Part 2, Behavioral section and the full question bank. This week is about breadth and depth simultaneously. The behavioral questions in AI PM loops have a specific AI texture — they are often asking about situations involving technical uncertainty, safety concerns, stakeholder misalignment around model performance, or decisions made with incomplete information. Generic behavioral prep is not sufficient.

Practice prompts for this week: Tell me about a time you had to make a product decision with incomplete data. What was the decision, what information did you have, and how did you decide? Tell me about a time a product feature you owned failed. What happened, what was your role in the failure, and what did you change? Tell me about a time you had to push back on a business stakeholder about a technical constraint. How did you handle it and what was the outcome?

Interview signal to sharpen this week: specificity in behavioral answers. The most common failure mode in behavioral interviews is vagueness — the answer that describes what you generally do rather than what you specifically did in a particular situation. Every behavioral answer should have a named context, a specific decision point, a real outcome, and a genuine reflection. Practice this structure until it is natural.

This week is not for learning new material. It is for integration. Run full mock interview sessions — ideally with a partner who can push back on your answers and probe for depth.

If you do not have a partner, record yourself answering questions from the Part 2 bank and review the recordings critically. The goal is to identify and close the gaps between how you think you answer and how you actually answer.

Also this week: return to outcomeos.online and retake the PM assessment. The delta between your Week 1 baseline and your Week 6 result is your measurable proof of growth.

The platform’s AI mentor can run targeted practice sessions on your remaining weak areas.

If you are in a cohort, this is your final preparation week before the graduation evaluation and your W3C Verifiable Skill Passport — cryptographically signed proof of AI PM competency that you can share with prospective employers. Unlike a LinkedIn certification or a course badge, it cannot be faked.

The outcomeos.online simulator is particularly useful this week for hallucination judgment practice — it plants deliberate errors in AI outputs and tests whether you catch them and reason correctly about them. That specific judgment is something MCQ-style assessments cannot test. It is also something that comes up in real AI PM work constantly, and in some interview loops directly.

Use the rest of this week for offer prep: understanding equity structures at AI companies, asking the right questions in final rounds, and calibrating compensation expectations for the current market.

These 5 Questions Cold Before you move to Week 2 of this calendar, close this guide and answer these questions without notes. Do not look anything up. Do not prepare a speech. Just answer, out loud or in writing, as if an interviewer asked them. 1. What is the difference between RAG and fine-tuning, and how would you decide which one to use for a specific product feature? 2. A model you shipped has high average accuracy but users are calling it unreliable.

What might explain that, and what would you change? 3. You have a feature that costs $0.06 per inference call. Your product has 800,000 daily active users who each trigger an average of 4 calls per day. What is your monthly inference cost and how do you think about whether that is acceptable? 4. What does “hallucination” mean in a language model, and what are two product design decisions you can make to reduce its impact on users? 5. What is a context window, and why does its size matter for the product decisions you make?

If you can answer all five without struggling, you are ready for Week 2. If two or more feel shaky, spend another day on Chapter 3 before moving forward.

The foundation matters. The rest of this guide builds on it.

Part 2 continues with 125 practice questions, worked answers, company-specific interview preparation, and offer navigation.