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1.0BeginnerModule 01 · Think Like a Strategist

The AI Mental Model

Understand what you are working with before you work with it

The prompt

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1.0The AI Mental Model
I want to understand how to work with you more effectively. My situation: [WHO I AM - role, industry, company stage] What I typically use AI for: [YOUR MAIN USE CASES] Based on this, tell me: 1.Where am I most likely leaving quality on the table? 2.What context should I always give you that I probably forget? 3.What is one thing I can do differently from today that would improve every output I get from you? Be direct. Assume I am smart but new to working with AI well. How to improve AI output? Define Role Specifies the AI's perspective, leading to more tailored responses. Provide Context Informs the AI about the situation, ensuring relevance. Set Expectations Guides the AI towards the desired output format and content.
LabelsReplace these

LabelsReplace these

Pro tip, from the book

The most common mistake is treating a bad output as a model failure. It is almost never the model. It is almost always a missing Role, thin Context, or no stated Expectation. Diagnose before you retry.

Who should run this prompt

Module role profile

Who should reason through this problem? The quality of your thinking is shaped by whose lens you borrow. A strategist and a risk analyst look at the same problem and see completely different things — both useful, both necessary. Before you prompt, choose who you need in the room. These profiles give you six internal perspectives and four outside views that challenge your assumptions before you commit to them.

The roles below are who the book suggests running this prompt — this lesson’s prompt doesn’t open with a single swappable role clause, so pick one and adapt the prompt’s own wording yourself.

Internal rolesThink from inside the organisation
Chief Strategy OfficerLong-term position, trade-offs, competitive moatSetting 3-year direction or evaluating a major pivot
McKinsey Senior PartnerMECE structure, evidence-first, no fluffStructuring a complex problem or board presentation
Product Strategy DirectorMarket timing, feature bets, customer signalDeciding what to build next and why
Strategy ManagerTranslating direction into quarterly prioritiesBreaking a big goal into executable 90-day plans
Research AnalystData quality, pattern recognition, no assumptionsValidating a hypothesis before acting on it
First Principles CoachStripping assumptions, rebuilding from fundamentalsWhen every solution feels like a variation of the same thing
Outside perspectivesChallenge your blind spots
Sceptical InvestorFinds the hole in every planBefore pitching or committing resources
Military Decision AnalystSpeed under uncertainty, clear action biasIn fast-moving situations with incomplete data
Devil's AdvocateArgues the opposite as hard as possibleWhen your team agrees too quickly
Socratic FacilitatorNever answers — asks until you find it yourselfWhen you need to think, not just receive an answer

◆◆◆◆◆ → ◆ seniority, board level down to specialist outside the organisation

How to make any role sharper

  1. Add years of experience: "...with 15 years in enterprise SaaS" produces different depth than just the title.
  2. Add what they care most about: "You care most about [X]" shapes every word of the output.
  3. Add their communication style: "Be direct. Flag risks first. No jargon." changes the tone entirely.
See also: 1.3 Role Prompting - The Expert Chair

Use this when

The problem it solves

You are new to AI, your outputs feel unpredictable, or you need to explain how AI works to your team in a way that actually helps them use it. An LLM is a prediction engine, not a thinking machine. It has read an enormous amount of human text and learned to predict what a useful response looks like given what you gave it. It has no memory between sessions. It cannot verify facts. It has no agenda. It is extraordinarily good at one thing: producing structured, relevant output when given clear structure and rich context. Three things determine every output you get: Role: who the AI is reasoning as. Vague role = generic output. Context what the AI knows about your situation. Missing context = missing relevance. Expectation what a good answer looks like. No expectation = AI guesses. As AI evolves from prompting to context engineering to agentic systems these three requirements stay constant. The technology changes. The need for clear human intent does not.

The method, in four moves

Do these in order
1

Before every AI session, ask: have I given it a Role, Context, and Expectation?

2

When output feels wrong, diagnose which of the three is missing do not rewrite randomly.

3

When introducing AI to your team, use this page as the one-minute briefing.

4

Return here whenever AI feels unpredictable. The answer is almost always in these three.

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