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Selected:ARID (Active Reviews for Intermediate Designs)Shape-First ModelingLearning CardExperiment Canvas
| Criterion |  Architecture ARID (Active Reviews for Intermediate Designs) |  Knowledge Modeling Shape-First Modeling |  Product Discovery Learning Card |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | The Learning Card connects traceable test results to a reasoned next action while keeping the limits of the insight visible. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | Medium | Low | Low |
Timedifferent | 2–4 h review as a planning range; preparation and evaluation additional | Halber Tag pro Domain Slice | 25-40 min | 30-60 min |
Participantsdifferent | Designers, developers as users, and facilitation | 1-4 | 1-5 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Prioritized development tasks, Jointly produced code or pseudocode, Observed difficulties and open design questions, Proposed design revisions | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Learning Card with evidence and next action | Completed Experiment Canvas, Success Metric |
Tags1 shared | ArchitectureEngineeringFeedbackValidationTasks | Knowledge graphValidationSemantic | ExperimentsValidationDiscoveryLearning | ExperimentsValidationDiscoveryHypothesis |