Writing PRDs for AI Coding Tools
Claude, ChatGPT, Cursor, Replit, and other AI tools can build incredible software—but only if you tell them exactly what you want. Here's how to write PRDs that AI actually understands.
Why AI Needs Structured PRDs
You've probably tried asking ChatGPT or Claude to "build me an app." Maybe you got some code back. Maybe it even worked... sort of. But was it what you actually wanted?
Here's the thing about AI coding tools: they're incredibly capable, but they're not mind readers. When you say "build a task management app," an AI might generate something completely different from what you imagined. Not because it's bad at coding—but because "task management app" means a hundred different things.
The fundamental truth of AI coding:
Garbage in, garbage out. Vague requirements produce vague results. Specific, structured requirements produce exactly what you want.
AI tools don't have your business context. They don't know your users. They can't see the UI mockup in your head. They don't know you prefer Tailwind over plain CSS, or that your team uses a specific folder structure. Everything you don't specify, AI will assume.
What happens without a structured PRD:
- AI uses different tech stack than your project
- Features work differently than you expected
- Edge cases aren't handled (or handled wrong)
- Endless back-and-forth to fix misunderstandings
- Code doesn't integrate with your existing codebase
What happens with an AI-ready PRD:
- Code matches your tech stack and patterns
- Features work exactly as specified
- Edge cases handled correctly from the start
- First output is usable (or close to it)
- Drops right into your existing project
The AI-Ready PRD Checklist
Before you hand your PRD to an AI coding tool, make sure it includes these essential elements:
Technical Context
Tech stack, framework version, existing libraries, folder structure, coding conventions
Data Models
Database schema, API structures, type definitions, relationships between entities
Explicit Requirements
Specific, measurable, unambiguous feature descriptions with exact behaviors
Acceptance Criteria
Testable conditions that define when a feature is complete
Edge Cases
What happens with empty data, errors, timeouts, invalid input, concurrent users
Examples
Sample data, code patterns to follow, API response formats, UI references
Setting Technical Context
The first thing AI needs to know is what it's working with. Think of this as giving the AI a map of your project before asking it to build something new.
Always include:
- Frontend: Next.js 14, TypeScript, Tailwind CSS
- Backend: Next.js API routes
- Database: PostgreSQL with Prisma ORM
- Auth: NextAuth.js with JWT sessions
- /app - Next.js App Router pages
- /components - Reusable UI components
- /lib - Utilities and API clients
- /prisma - Database schema
- Use 'use client' directive for client components
- Prefer named exports over default exports
- Use Tailwind classes, no inline styles
- Error handling with try/catch, log to console
Pro Tip:
If you're using Cursor, it already has context from your open files. But for Claude or ChatGPT, paste relevant existing code at the start so AI can match your style.
Writing Requirements AI Understands
The way you phrase requirements matters. AI interprets language literally, so precision is key.
The EXACT Framework
When writing each requirement, make sure it's:
E - Explicit
State exactly what should happen. No room for interpretation.
X - eXamples included
Show, don't just tell. Include sample data and expected outputs.
A - Acceptance criteria defined
How will you know it's done? List testable conditions.
C - Constraints specified
What are the limits? Max length, allowed values, performance requirements.
T - Technical details provided
Field names, data types, API endpoints, error codes.
Words to Avoid vs. Words to Use
Avoid (Too Vague)
- "Make it fast"
- "User-friendly interface"
- "Handle errors appropriately"
- "Nice design"
- "Secure authentication"
- "Display data properly"
Use Instead (Specific)
- "Response time under 200ms"
- "Form validates on blur, shows inline errors"
- "Show toast notification with error message for 5s"
- "Use Tailwind, rounded corners, dark theme"
- "JWT tokens, 24hr expiry, httpOnly cookies"
- "Table with columns: Name, Email, Created date"
Good vs. Bad: Real Examples
Let's look at the same feature written two different ways—and see how AI interprets each.
Example: User Login Feature
Bad PRD
Create a login page. Users should be able to log in with email and password. Show errors if something goes wrong. Make it look nice.
AI will guess: What framework? What happens on success? What errors? What's "nice"?
Good PRD
- Email: type="email", required, validate format on blur
- Password: type="password", required, min 8 chars
- Submit button: disabled until form valid
- On submit: show loading spinner on button
- Success: redirect to /dashboard
- Error: show red text below form with message from API
- Rate limit: disable form for 30s after 5 failed attempts
- Centered card, max-w-md
- Dark background (bg-dark-100)
- Crimson accent for primary button
Tool-Specific Tips
Different AI tools have different strengths. Here's how to optimize your PRD for each:
Claude (Anthropic)
Best for: Complex implementations, full features
- • Claude excels at understanding context—give it the full picture
- • Can handle very long PRDs with multiple features
- • Great at following existing code patterns when you provide examples
- • Ask it to "think step by step" for complex logic
- • Use Claude Projects or paste your PRD for persistent context
ChatGPT / GPT-4 (OpenAI)
Best for: Explanations, code snippets, iteration
- • Break large PRDs into smaller chunks for best results
- • Great at explaining what code does and why
- • Use Custom GPTs to save your tech stack context
- • Good for brainstorming before finalizing PRD
- • Canvas feature useful for iterating on code
Cursor
Best for: Working within existing codebases
- • Automatically has context from your open project
- • Reference files with @filename in your prompts
- • Use Composer for multi-file changes from PRD
- • Great for implementing one feature at a time
- • Add PRD to .cursor/rules for persistent context
Replit Agent
Best for: Full app generation and instant deployment
- • Builds complete applications from natural language PRDs
- • Handles frontend, backend, and database setup automatically
- • Instant deployment—see your app live immediately
- • Great for MVPs and prototypes from scratch
- • Include deployment requirements in your PRD (environment variables, domains)
Common Mistakes to Avoid
After helping thousands of people generate PRDs, we've seen these mistakes over and over:
1. Assuming AI knows your context
AI doesn't know you're building a B2B SaaS, or that your users are developers, or that you prefer minimal UI. State everything explicitly.
2. Skipping edge cases
What if the list is empty? What if the user submits twice? What if the API times out? AI won't anticipate these unless you list them.
3. Using subjective language
"Fast," "clean," "modern," "intuitive"—these mean different things to everyone. Replace with specific, measurable criteria.
4. Forgetting the unhappy path
PRDs often focus on what happens when everything works. But what happens on error? Invalid input? Network failure? Define these too.
5. Not providing examples
A sample API response or data structure is worth a thousand words. AI learns patterns from examples and will match what you show.
Frequently Asked Questions
What is an AI-ready PRD?
Which AI coding tools work best with PRDs?
How detailed should my PRD be for AI tools?
Can AI build my entire app from a PRD?
What's the biggest mistake people make with AI PRDs?
Should I include code examples in my PRD?
How do I handle changes after AI starts building?
What technical details should I always include?
Ready to Generate Your AI-Ready PRD?
rapidPRD creates structured, detailed PRDs that AI coding tools love. Get a complete document in minutes—ready to paste into Claude, ChatGPT, Cursor, or Replit.