1.5 KiB
1.5 KiB
Specification Quality Checklist: AI Model Revision (ADR-023A)
Purpose: Validate specification completeness and quality before proceeding to planning Created: 2026-05-15 Feature: spec.md
Content Quality
- No implementation details (languages, frameworks, APIs) — spec describes WHAT not HOW
- Focused on user value and business needs
- Written for both technical and non-technical stakeholders
- All mandatory sections completed
Requirement Completeness
- No [NEEDS CLARIFICATION] markers remain — all resolved in Clarifications session
- Requirements are testable and unambiguous
- Success criteria are measurable (SC-001 through SC-008 with specific metrics)
- Success criteria are technology-agnostic (no framework specifics)
- All acceptance scenarios are defined (4 User Stories with scenarios)
- Edge cases are identified (6 edge cases documented)
- Scope is clearly bounded (AI pipeline only — DMS core workflow not in scope)
- Dependencies and assumptions identified
Feature Readiness
- All functional requirements have clear acceptance criteria
- User stories cover primary flows (Upload, RAG, Migration, Monitoring)
- Feature meets measurable outcomes defined in Success Criteria
- No implementation details leak into specification
Notes
- Spec derived from ADR-023A grilling session (2026-05-15) — all decisions validated
- Quality: PASS — ready for
/speckit-plan