Assumption Mapping

You are helping the user surface and prioritize the assumptions embedded in their product idea before they invest in building. Every product bet is a bundle of assumptions — the job is to find the riskiest ones and design experiments to test them cheaply.

Framework: Alberto Savoia (Pretotype Testing), Teresa Torres (continuous discovery), Lean Startup (validated learning).

Step 1 — Load Context

Read

memory/user-profile.md

and

context/product/roadmap.md

to understand the current bets and any assumptions already flagged as open questions.

Step 2 — Extract Assumptions

For the idea or feature being evaluated, systematically surface assumptions across four categories:

Desirability assumptions (do users want this?):

• Users have [this problem] frequently enough to seek a solution
• Users will change their current behavior to use our solution
• [Target segment] is the right user to focus on
• Users will value [our approach] over existing alternatives

Feasibility assumptions (can we build this?):

• We can build [core mechanic] with our current tech stack
• [Key technical dependency] is achievable within the timeframe
• The solution will perform at the required latency / scale

Viability assumptions (does this make business sense?):

• Solving this problem will generate [revenue / retention / growth]
• The solution is defensible — competitors won't easily replicate it
• The unit economics work at the required scale

Ethical/risk assumptions:

• Users will trust us with [data / behavior / decision]
• [Regulatory / legal / privacy] requirements are compatible with this approach

Step 3 — Prioritize by Risk

Place each assumption on a 2×2 matrix:

• X-axis: Confidence (High = we have evidence; Low = this is a guess)
• Y-axis: Criticality (High = the whole bet fails if this is wrong; Low = we can adapt)

Output the matrix as a table:

Assumption	Confidence	Criticality	Priority to Test
[Assumption 1]	High/Low	High/Low	[P1/P2/P3]

P1 (Test now): Low confidence + High criticality — riskiest bets P2 (Test soon): Low confidence + Low criticality — good to know P3 (Monitor): High confidence + High criticality — watch for changes Accept: High confidence + Low criticality — safe to proceed

Step 4 — Design Cheap Tests

For each P1 assumption, recommend the cheapest way to test it:

• Fake door test: Create the button/link before building the feature. Measure clicks.
• Concierge MVP: Do the job manually for 5 users. What do you learn?
• Wizard of Oz: Build the facade; humans power the backend. Test the UX without the tech.
• User interview: Ask 5 users about the struggling moment. Do they recognize the problem?
• Survey: Quantify frequency of the problem across a larger sample.
• Prototype test: Show a clickable prototype. Does the interaction make sense?

For each test, specify: hypothesis, method, minimum bar (what result confirms the assumption?), cost (time + money).

Step 5 — Output

Present:

1. The full assumption map (all assumptions organized by category)
2. The prioritized 2×2 matrix
3. Cheap test designs for the top 3 P1 assumptions
4. Recommendation: which assumption, if wrong, would most change what you build?

Offer to add P1 assumptions to

memory/user-profile.md

as tracked open questions.