Vibe Coding | 2026-07-22 | 9 min read
Before You Ask AI to Build an App, Write These 5 Docs
AI coding agents build better apps when you give them product requirements, technical choices, app flow, design direction, data schema, and a testable build plan.
Direct answer: Before asking AI to build an app, write five lightweight documents: a PRD, a technical design doc, an app-flow map, a design brief, and a backend/schema plan. Then add an engineering plan that breaks the work into small testable steps.
Written by: Esmail Hanif, AI Visibility Strategist & Founder, Martecks
Short answer
Do not ask an AI coding agent to build the app while the idea is still fuzzy. The agent will fill in the gaps, and those guesses become your product.
Before building, write five lightweight docs: a PRD, a technical design doc, an app-flow map, a design brief, and a backend/schema plan. Then turn them into an engineering plan with small testable tasks.
Why docs matter more with AI
With a human engineer, unclear requirements become meetings. With an AI coding agent, unclear requirements become code.
That is why context engineering matters so much in app building. The prompt is not enough. The agent needs product intent, user flows, technical constraints, design direction, data shape, build order, and acceptance criteria.
Good docs do not slow vibe coding down. They prevent the expensive version of speed: rebuilding the same app three times because the first version guessed the wrong product.
The 5 docs
Keep these short. You are creating enough context for the agent to build the right first version, not writing a corporate binder.
| Doc | What it answers | What AI gets wrong without it |
|---|---|---|
| PRD | What are we building, for whom, and why? | It builds features that sound useful but do not serve the real user. |
| Technical design doc | What stack, APIs, auth, integrations, and constraints matter? | It chooses random libraries, architecture, or storage patterns. |
| App-flow map | What screens exist and what happens after each action? | It creates disconnected screens or dead-end buttons. |
| Design brief | What should the app feel like visually and structurally? | It defaults to generic cards, gradients, and inconsistent UI. |
| Backend/schema plan | What data exists, how is it related, and who can access it? | It creates weak tables, missing fields, or risky permissions. |
PRD
The product requirements document explains the app in plain language. It should include the user, problem, core jobs, must-have features, out-of-scope features, success criteria, and constraints.
For AI, the most useful part is scope. If you do not say what not to build, the agent may overbuild because extra features look helpful in isolation.
- Who is the user?
- What problem does the app solve?
- What is the smallest useful version?
- What should not be built yet?
- What does success look like?
Technical design doc
The technical design doc tells the agent how the app should be built. It names the stack, packages, APIs, data storage, auth rules, deployment target, environment variables, and technical constraints.
This is where you prevent random architecture. If the repo already uses React, Vite, Supabase, Stripe, or Cloudflare Pages, say that. AI works better when it follows the existing system instead of inventing a new one.
App-flow map
The app-flow map describes screens and user journeys. It says where the user starts, what they click, what data they enter, what states they see, and where the flow ends.
This is especially important for apps, agents, and automation workflows because most bugs happen between screens or steps, not inside a single pretty page.
| Flow area | Questions to answer |
|---|---|
| Onboarding | What does the user enter first? What can be skipped? |
| Main workflow | What is the primary loop the app supports? |
| Empty states | What appears before data exists? |
| Error states | What happens when an API, upload, login, or payment fails? |
| Success state | How does the user know the job is complete? |
Design brief
A design brief gives the AI taste. It should include audience, tone, layout references, colors, typography, component rules, spacing, and what to avoid.
Without this, AI tends to build the same safe interface: rounded cards, vague badges, generic gradients, and oversized hero copy. A design brief gives the model a target instead of letting it average the internet.
Backend and schema plan
The backend/schema plan maps the real information the app needs: tables, fields, relationships, permissions, events, files, and external services.
This is where many vibe-coded apps become risky. A working demo is not enough if the data model is confused, user data is exposed, or every future feature requires breaking the schema.
Bonus engineering plan
After the five docs, create an engineering plan. This turns the product into ordered, testable work.
The plan should break the build into small tasks, name dependencies, define acceptance criteria, and say how each task will be checked. This is where an AI coding agent becomes useful instead of chaotic.
- Task name.
- Files or areas likely affected.
- Dependencies.
- Acceptance criteria.
- Manual QA step.
- Automated test or build check.
- Human review needed or not.
Simple template
Use this as the first version before giving the project to Codex, Claude Code, Cursor, or another coding agent.
| File | Keep it to |
|---|---|
| PRD.md | Problem, user, scope, features, non-goals, success criteria. |
| TECHNICAL-DESIGN.md | Stack, architecture, APIs, auth, deploy target, constraints. |
| APP-FLOW.md | Screens, routes, states, user journeys, edge cases. |
| DESIGN-BRIEF.md | Visual direction, components, spacing, references, avoid list. |
| SCHEMA.md | Tables, fields, relationships, permissions, events. |
| ENGINEERING-PLAN.md | Build order, tasks, acceptance criteria, tests, QA. |
App, agent, or automation workflow?
This method works for all three, but the emphasis changes.
For an app, the app-flow and design brief matter more. For an agent, the tool rules, state, approvals, and evals matter more. For an automation workflow, the trigger, data handoff, error handling, and human review point matter more.
| Build type | Most important planning docs |
|---|---|
| App | PRD, app-flow map, design brief, schema plan. |
| Agent | PRD, technical design, tool rules, state machine, eval plan. |
| Automation workflow | Trigger map, data schema, approval rules, error handling, outcome metric. |
Reference links
These sources support the planning, context, and AI coding workflow behind this article.
Sources: OpenAI: prompt engineering guide, OpenAI: code generation guide, Anthropic: Claude Code overview, Atlassian: product requirements document
Final answer
Before asking AI to build an app, give it the context a good builder would ask for anyway.
Write the PRD, technical design, app flow, design brief, backend/schema plan, and engineering plan. Then let the agent build one small testable step at a time.