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This locks the surface area. When the agent later suggests adding social login or WebSockets, you point to the non-goals list and stay on track. Treat the PRD as the single source of truth. If a requirement must change, update the document first, then regenerate the plan. This discipline keeps the project from becoming a pile of disconnected experiments that bypass the original requirements.
Layer 2 — Plan Vertical Slices Before Prompting
Once the PRD is frozen, ask the agent to generate a step-by-step actionable plan that builds the app in phases using a vertical slice method. Each slice must span the full stack—database schema, API contract, and UI—ordered by increasing complexity so that every phase ships a demonstrable feature. Resist the temptation to let the agent batch all schema work first; a vertical slice forces integration from day one. A typical prompt for this looks like:
Given the locked PRD, output an implementation plan as numbered vertical slices. Each slice must include schema changes, API routes, and UI components. Order by complexity so slice 1 is end-to-end and deployable.
An example first slice for a project tracker might contain:
CREATE TABLE projects (
id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
name text NOT NULL,
created_at timestamptz DEFAULT now()
);
app.post('/projects', async (c) => {
const body = await c.req.json();
const row = await db.insert(projects).values(body).returning();
return c.json(row, 201);
});
export default function ProjectsPage() {
const { data } = useQuery({ queryKey: ['projects'], queryFn: fetchProjects });
return <ul>{data?.map(p => <li key={p.id}>{p.name}</li>)}</ul>;
}
Keep this plan in the agent's context window as a strict sequence. Do not let the LLM decide slice ordering or introduce unplanned architecture without validation; the human maintains the roadmap. Enforce compile gates at every slice (see Layer 3). Working in integrated slices surfaces mismatches between the schema and the UI immediately, whereas a layer-by-layer approach often leaves you with orphaned tables and unused endpoints that are harder to prune once the codebase grows.
Layer 3 — Gate Every Slice and Maintain a Living Context Map
Treat every vertical slice as a deployable unit that must exit cleanly before the next slice begins. Running the TypeScript compiler with no emit after each slice closes the local-to-deploy gap and catches type errors while they are still cheap to fix.
npx tsc --noEmit
If the command exits non-zero, fix the errors immediately; do not prompt for the next slice until it returns zero. This prevents error accumulation at the point of introduction and keeps the branch in a shippable state. A clean gate means you can deploy any completed slice on demand without a surprise integration phase or hidden type regressions.
If you let the data model drift, one bad assumption can infect every subsequent slice. Maintain your own mental map of how the pieces connect and keep a living context document updated after each slice. Append a brief summary of what was built, which files changed, and any deferred decisions or open questions.
## Slice 3: Checkout flow
- Added `order.ts` schema, `createOrder` server action
- Deferred: payment webhook retry logic
- Next: inventory decrement on confirmation
Feed this summary back into the next prompt so the agent does not hallucinate cross-slice dependencies. When the agent sees the exact state of the system, it generates code that respects existing contracts rather than inventing new ones. This discipline turns a prototype stream into a production branch.
References
The sources below support the workflow described above. Each reference corresponds to a specific layer or gate in the PRD-to-prompt pipeline. SitePoint, "Production Vibe Coding Workflow: From Prompt to Deploy" — https://www.sitepoint.com/production-vibe-coding-workflow. This article outlines the end-to-end deployment path for vibe-coded projects and introduces compile gates as a mechanism for closing the local-to-production gap before errors compound across the codebase. AIM Consulting, "Vibe Coding in Practice: Patterns, Pitfalls, and Prompting Strategies" — https://aimconsulting.com/insights/vibe-coding-practice-patterns-pitfalls-prompting. The authors identify iterative refinement and structured prompting as critical patterns for maintaining code quality when working with AI agents across multiple generation sessions. Reddit r/ClaudeAI, "The Ultimate Vibe Coding Guide" — https://www.reddit.com/r/ClaudeAI/comments/1kivv0w/the_ultimate_vibe_coding_guide. This practitioner guide emphasizes the importance of a detailed initial vision and clear input constraints before engaging an LLM on implementation tasks. Reddit r/vibecoding, "Anyone else tired of starting vibe coding projects that turn into complete disasters halfway through?" — https://www.reddit.com/r/vibecoding/comments/1nq2clq/anyone_else_tired_of_starting_vibe_coding. The thread documents common failure modes when context is lost mid-project, reinforcing the need for a maintained mental map of architecture and data flow. Dev.to/wasp, "A Structured Workflow for 'Vibe Coding' Full-Stack Apps" — https://dev.to/wasp/a-structured-workflow-for-vibe-coding-full-stack-apps-352l. This post defines the vertical slice method adapted for LLM-assisted development, progressing complexity from database schema to user interface in discrete, testable phases.
References for further reading
Sources consulted while researching this guide, included so you can verify the details and go deeper. Listing them is not a claim that every line was independently fact-checked.
I packaged the setup above into a ready-to-use kit — **Spec-to-Ship: The Vibe Coder's Build Kit (16 Items)* — for anyone who'd rather copy-paste than wire it from scratch: https://unfairhq.gumroad.com/l/euyyyi.*