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Criterion
Paper illustration for Solution Interview
Product Discovery
Solution Interview
Four paper stations connect system purpose, quality goals, risk cards and focused investigation with a target and gap profile.
Architecture
LASR (Lightweight Approach for Software Reviews)
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Paper illustration for Fake Door Test
Product Discovery
Fake Door Test
Purposedifferent
When a solution idea is already on the table, it checks how it is understood and evaluated. It looks for resonance, objections, and missing fit early in the conversation.LASR helps development teams identify where their software falls short of important quality goals and which improvements to consider. A small group with existing system knowledge can make an initial assessment and investigate open questions in more detail.The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible.When demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small.
Complexitydifferent
MediumMediumLowMedium
Timedifferent
30-60 min je Interview3–4 h for initial assessment; preparation and deeper analysis additional25-40 min1-5 Tage
Participantsdifferent
5-10 Interviews1 or a small team; particularly suitable for 3–4 people1-5Nutzertraffic
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Solution Feedback, Objection List, Validation NotesLean Mission Statement describing the software’s purpose, Prioritized quality goals and evaluation criteria, Risks and estimated gaps against quality goals, More closely examined findings and open checksLearning Card with evidence and next actionClick Data, Interest Signal, Learning Decision
Tagsno overlap
ValidationInterviewsPrototype
ArchitectureQualityRiskAssessmentLean
ExperimentsValidationDiscoveryLearning
ValidationExperimentsDemandDiscovery
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