speckit.clarify

Clarify a feature specification by running a structured questioning loop that asks up to 5 targeted questions to detect ambiguities across functional, data, UX, and non-functional domains. Accepted answers are encoded directly back into the spec to ensure traceable decisions.

Software requirements lead to QA rework when functional requirements and terminology are underspecified. A targeted clarification workflow prevents this by detecting ambiguities early and establishing measurable definitions of done before development begins.

Update a specification for traceability by appending accepted answers to a dedicated Clarifications section after each targeted question. This finite loop ensures every clarified decision is encoded back into the spec with measurable definitions of done.

Yes, a structured questioning workflow defines non-functional attributes and UX flows by detecting ambiguities across these specific domains. It generates up to 5 targeted clarification questions to ensure all spec areas have measurable definitions of done.

The best way to resolve underspecified terminology in a feature spec is to invoke a structured clarification loop that asks targeted questions. This workflow identifies ambiguous terms and updates the specification after each accepted answer to ensure traceable decisions.

The limitation of this clarification loop for requirements gathering is its finite scope of up to 5 targeted questions per iteration. If a feature specification has extensive ambiguities across data models and UX flows, multiple sequential loops are required to achieve full traceability.