Page 23 · Module 01, Think Like a Strategist · this screen continues that page
All 108 frameworksThe AI Mental Model
Understand what you are working with before you work with it
The prompt
Copy & paste readyLabelsReplace 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 profileWho 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.
◆◆◆◆◆ → ◆ seniority, board level down to specialist◈ outside the organisation
How to make any role sharper
- Add years of experience: "...with 15 years in enterprise SaaS" produces different depth than just the title.
- Add what they care most about: "You care most about [X]" shapes every word of the output.
- Add their communication style: "Be direct. Flag risks first. No jargon." changes the tone entirely.
Use this when
The problem it solvesYou 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 orderBefore every AI session, ask: have I given it a Role, Context, and Expectation?
When output feels wrong, diagnose which of the three is missing do not rewrite randomly.
When introducing AI to your team, use this page as the one-minute briefing.
Return here whenever AI feels unpredictable. The answer is almost always in these three.
Pairs well with
As printed with this frameworkModule 01 — Think Like a Strategist
Learn to work with AI. Then learn to think. Both are non-negotiable.
13 frameworks, printed on pages 22–47.
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