An architecture blueprint is the artifact that sits between a PRD and code. It defines the data models, API endpoints, and component relationships implied by the feature requirements. Without it, AI coding tools like Cursor and Claude Code invent architecture on the fly — producing code that works feature-by-feature but becomes incoherent at system scale. Scriptonia generates the blueprint from the PRD in 15–25 seconds. This is the workflow that prevents the vibe coding three-month wall.
The vibe coding problem this solves
Teams using AI coding tools to ship at speed hit a predictable inflection point around 90 days: the codebase has become incoherent. Features conflict with each other because each was coded with different assumptions about data models. The authentication system was designed one way in week 1 and a different way in week 6. Nobody documented what the system actually does.
The root cause is not the AI coding tool — it is the absence of a system architecture document that the AI can use as consistent context. Each coding session starts without memory of previous sessions. Without a shared architecture document, the AI makes local decisions that are globally inconsistent.
An architecture blueprint fed into the system context of every coding session prevents this. The AI makes decisions consistent with the defined data model, the defined API contracts, and the defined component boundaries. The codebase stays coherent.
What a Scriptonia architecture blueprint contains
Data model
The entities and relationships implied by the PRD's user stories and success metrics. For each entity: fields, types, constraints, and relationships. Written in a format readable by both engineers and AI coding tools.
Example (for a PRD review workflow feature):
PRD: { id, title, content, status: enum(draft, in_review, approved, rejected), created_by, created_at, updated_at }
Review: { id, prd_id → PRD, reviewer_id → User, status: enum(pending, approved, changes_requested), comment, reviewed_at }
ReviewAssignment: { id, prd_id → PRD, user_id → User, role: enum(required, optional), assigned_by, assigned_at }
API endpoints
The REST endpoints implied by the feature. For each endpoint: method, path, auth requirements, request body, and response shape.
POST /api/prds/{id}/reviews— Submit a review. Auth: Bearer. Body: {status, comment}. Returns: Review object.
GET /api/prds/{id}/reviews— List reviews for a PRD. Auth: Bearer. Returns: Review[].
POST /api/prds/{id}/reviewers— Assign reviewer. Auth: Bearer (owner or admin). Body: {user_id, role}. Returns: ReviewAssignment.
PATCH /api/prds/{id}/status— Update PRD status. Auth: Bearer (auto-transition when all required reviewers approve). Returns: PRD with updated status.
Component relationships
How the frontend components relate to each other and to the API. Written as a component tree with data dependencies.
PRDDetailPage→ fetches PRD + reviews via GET /api/prds/{id} + GET /api/prds/{id}/reviews
ReviewPanel(child of PRDDetailPage) → displays reviewer list and status; triggers POST /api/prds/{id}/reviews
ReviewerAssigner(child of PRDDetailPage, owner-only) → triggers POST /api/prds/{id}/reviewers
ApprovalStatusBadge→ computed from reviews array; shows: awaiting N reviewers / changes requested / approved
State machine
For features with status transitions, the blueprint includes a state machine diagram describing valid transitions and their triggers.
draft → in_review: triggered by POST /api/prds/{id}/reviewers (first reviewer assigned)
in_review → approved: triggered automatically when all required reviewers submit approved status
in_review → changes_requested: triggered when any required reviewer submits changes_requested
changes_requested → in_review: triggered when PM updates the PRD (re-notifies all reviewers)
approved → draft: triggered by PM explicitly (resets all review statuses)
How to use the blueprint with Cursor or Claude Code
Paste the full architecture blueprint into the system prompt or project context of your AI coding session. For Cursor: add it to .cursorrules or as a project document. For Claude Code: paste it as the first message in a new session, or reference it in CLAUDE.md.
The AI coding tool will use the data model to generate consistent migrations, use the API endpoints to generate consistent route handlers, and use the component relationships to generate components with the correct data dependencies.
The most important section for AI coding tools is the data model. Consistent entity definitions prevent the most common vibe coding failure: the same concept represented with different field names in different parts of the codebase.