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Criterion
Paper illustration of Shape-first Modeling with its method-specific working model.
Knowledge Modeling
Shape-First Modeling
Design sheets and interface cards connect to jointly written pseudocode with highlighted unresolved questions.
Architecture
ARID (Active Reviews for Intermediate Designs)
Paper illustration of a Learning Card with four fields for hypothesis, observation, insight, and action.
Product Discovery
Learning Card
Paper illustration of MVP Test Matrix with a method-specific labelled workspace.
Product Discovery
MVP Test Matrix
Purposedifferent
Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start.ARID examines how well developers can work with a technical design. It focuses on a part of the system that can still be revised, between the architecture overview and the detailed specification.The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible.When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan.
Complexitydifferent
MediumMediumLowMedium
Timedifferent
Halber Tag pro Domain Slice2–4 h review as a planning range; preparation and evaluation additional25-40 min45-75 min
Participantsdifferent
1-4Designers, developers as users, and facilitation1-52-8
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape DocumentationPrioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisionsLearning Card with evidence and next actionTest Matrix, Test Plan
Tags1 shared
Knowledge graphValidationSemantic
ArchitectureEngineeringFeedbackValidationTasks
ExperimentsValidationDiscoveryLearning
ExperimentsValidationDiscoveryOptions
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