ask-questions-if-underspecified

To clarify underspecified requirements, prompt the user with 1–5 must-have questions to elicit missing details and confirm assumptions before proceeding. This prevents misinterpretation by resolving ambiguity in scope, acceptance criteria, and constraints just in time.

Prevent AI workflows from executing with ambiguous scope by requiring explicit invocation when underspecification is detected. This approach avoids automatic execution and ensures safe, auditable interaction by structuring questions around missing objectives and environment details.

Identify missing acceptance criteria and constraints by detecting underspecified requests and applying structured question templates. These templates guide users to provide the necessary scope, constraints, and environment details required to proceed safely.

You need to ask clarifying questions in AI-assisted workflows whenever uncertainty is detected regarding objectives, acceptance criteria, scope, constraints, or environment details. Prompting for these must-have details before acting ensures safe and auditable interaction.

Yes, this approach works without external dependencies or components. It relies entirely on structured questioning templates and explicit invocation logic to resolve underspecification across projects and AI-assisted workflows natively.

Project execution fails with missing environment details because automatic action leads to misinterpretation and wrong implementations. Requiring explicit invocation and structured questions resolves underspecification, ensuring objectives and constraints are confirmed beforehand.