Medical GraphRAG Assistant

# Specification Quality Checklist: AWS GPU-based NVIDIA NIM RAG Deployment Automation **Purpose**: Validate specification completeness and quality before proceeding to planning **Created**: 2025-11-09 **Feature**: [spec.md](../spec.md) ## Content Quality - [x] No implementation details (languages, frameworks, APIs) - [x] Focused on user value and business needs - [x] Written for non-technical stakeholders - [x] All mandatory sections completed ## Requirement Completeness - [x] No [NEEDS CLARIFICATION] markers remain - [x] Requirements are testable and unambiguous - [x] Success criteria are measurable - [x] Success criteria are technology-agnostic (no implementation details) - [x] All acceptance scenarios are defined - [x] Edge cases are identified - [x] Scope is clearly bounded - [x] Dependencies and assumptions identified ## Feature Readiness - [x] All functional requirements have clear acceptance criteria - [x] User scenarios cover primary flows - [x] Feature meets measurable outcomes defined in Success Criteria - [x] No implementation details leak into specification ## Validation Results ✅ **All quality checks PASSED** ### Content Quality Review - Spec avoids implementation-specific details (no mention of specific code, libraries except as requirements context) - Focused on user outcomes: deployment time, vectorization throughput, query response time - Written in business language accessible to non-technical stakeholders - All mandatory sections present and complete ### Requirement Completeness Review - No [NEEDS CLARIFICATION] markers present - all requirements are fully specified - Each functional requirement is testable (e.g., FR-001: provision GPU-enabled instances can be verified by checking instance type) - Success criteria include specific measurable targets (e.g., SC-001: 30 minutes, SC-004: 100 docs/min, SC-006: <1 second) - Success criteria are technology-agnostic, focusing on user-facing outcomes rather than internal implementation - All 5 user stories have detailed acceptance scenarios with Given/When/Then format - Edge cases comprehensively cover failure modes: GPU memory exhaustion, IP changes, API failures, etc. - Scope clearly bounded with comprehensive "Out of Scope" section - Dependencies and assumptions thoroughly documented ### Feature Readiness Review - Each FR maps to acceptance scenarios in user stories - User scenarios cover all critical paths: deployment (P1), vectorization (P2), image processing (P3), RAG queries (P2), validation (P1) - Success criteria are measurable and aligned with functional requirements - No implementation leakage detected - spec remains at requirements level ## Notes - Specification is complete and ready for `/speckit.plan` phase - No updates required - all checklist items pass validation - The spec successfully balances technical precision with business accessibility