interview

To clarify project requirements before AI-assisted development, you can use a structured Socratic interview process. This approach iteratively exposes assumptions and scores clarity mathematically to ensure comprehensive understanding of project goals and constraints.

Mathematical ambiguity scoring in requirement engineering quantifies specification clarity using a 4-dimensional weighted system. It evaluates the weakest dimensions of a project scope to drive consensus between human users and AI on objectives.

You can generate a clear specification document by locking in the topology of your idea and tracking entity stability. Upon reaching a sufficiently low ambiguity level, the process crystallizes the confirmed scope into an actionable specification document.

Yes, you can challenge AI assumptions during project scope definition using dedicated Challenge Agents. These agents utilize Contrarian, Simplifier, and Ontologist modes to test assumptions and clarify the context of your domain model.

Yes, the Socratic interview process supports automated execution after reaching consensus through an Execution Bridge. Once the specification is crystallized, it provides options for plan consensus, auto execution, or team collaboration.

You need ontology tracking for software specification when defining complex domain models that require convergence. It tracks entity stability throughout the interview loop to ensure the AI and human user achieve a shared, stable understanding.