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Hypothesis tree: break a fuzzy client question into testable branches

The client asked something huge and vague. Turn it into a MECE tree of hypotheses you can actually test this week.

The prompt — copy and run it

You are a strategy consultant structuring an ambiguous client question into a testable hypothesis tree — a thinking aid, not a conclusion.

Produce:

A) CORE QUESTION — the single decision the client actually needs to make, stated in one sentence.

B) HYPOTHESIS TREE — a MECE breakdown into 3-4 branches, each a falsifiable hypothesis (not a topic), and under each the 1-2 sub-hypotheses that would prove or kill it.

C) TEST PLAN — for each leaf hypothesis, the specific analysis or data that would confirm or refute it, and roughly how hard it is to get.

D) FIRST CUT — which 2 branches to test first for the fastest read on the answer, and why.

Inputs: [CLIENT QUESTION] · [WHAT WE ALREADY KNOW] · [DATA / ACCESS AVAILABLE] · [DEADLINE]

Rules: Keep branches genuinely mutually exclusive and collectively exhaustive — flag any overlap. Do not assert conclusions; these are hypotheses to test. Keep confidential client data out of consumer AI tools. The structure is a scaffold; the analysis and judgment stay yours. Do not invent facts, numbers, or details you weren't given; verify anything uncertain against the source before relying on it.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Fill in your inputs. Replace each bracketed placeholder with your specifics: [CLIENT QUESTION][WHAT WE ALREADY KNOW][DATA / ACCESS AVAILABLE][DEADLINE]
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Weak engagements boil the ocean because the question was never decomposed; forcing falsifiable hypotheses (not topics) into a MECE tree with an explicit test per leaf converts a vague ask into a prioritized work plan — and the overlap flag catches the double-counting that quietly wrecks a supposedly MECE structure.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

The client asked something huge and vague. Turn it into a MECE tree of hypotheses you can actually test this week.

Why does this prompt work?

Weak engagements boil the ocean because the question was never decomposed; forcing falsifiable hypotheses (not topics) into a MECE tree with an explicit test per leaf converts a vague ask into a prioritized work plan — and the overlap flag catches the double-counting that quietly wrecks a supposedly MECE structure.

What mistake does this prompt help you avoid?

Boiling-the-ocean scoping — a falsifiable, overlap-checked MECE tree with a per-leaf test plan forces prioritization before analysis begins.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.