How to Write a Technical PRD
for AI Code Generation
The quality of AI-generated code depends entirely on the quality of your requirements. This guide teaches you how to write Product Requirements Documents that AI coding assistants can actually understand and implement correctly.
The quality of AI-generated code tracks almost directly with the quality of your requirements. Give an AI tool a vague spec and you get vague code—it won't ask for clarification, it'll just guess. And those guesses compound.
Most PRDs were written for human developers who fill in gaps with experience and common sense. AI doesn't do that. It takes what you write literally, picks the simplest path when something's unclear, and moves on. The fix is to write more explicitly—not more formally, just more specifically.
This guide covers the seven sections that actually matter, how to write acceptance criteria AI can act on, and the mistakes that cause most of the "it works but it's wrong" frustration.
1Why Traditional PRDs Fail with AI
Traditional PRDs rely heavily on implicit knowledge—assumptions that any experienced developer would understand without being told. Consider this requirement:
Poor PRD Example:
"Users should be able to log in with their email and password."
A human developer would immediately think: password hashing, session management, rate limiting, password reset flow, input validation, error messages, and dozens of other considerations. An AI will generate a basic login form that stores passwords in plain text unless you explicitly tell it otherwise.
The fundamental rule of AI-friendly PRDs: If it's not explicitly stated, assume AI will take the simplest possible path. This isn't a flaw in AI—it's a feature. You want AI to be predictable, not creative, when implementing requirements.
2The 7 Essential Sections of an AI-Friendly PRD
Seven sections consistently lead to better code generation:
1. Project Context & Goals
What problem does this solve? Who is the target user? What does success look like? This helps AI understand the 'why' behind every feature.
Example:
A task management app for freelance designers who need to track client projects, deadlines, and billable hours. Success = users completing 80% of logged tasks.
2. Technical Constraints
Tech stack, third-party integrations, performance requirements, security needs. AI needs to know boundaries before generating code.
Example:
React 18 + TypeScript, Supabase for auth/database, must support offline mode via service workers, WCAG 2.1 AA compliance required.
3. Feature Specifications
Detailed breakdown of each feature with exact behavior. Not just 'what' but 'how'. Include edge cases explicitly.
Example:
Task creation: title (required, 1-100 chars), description (optional, max 2000 chars), due date (optional, must be future), priority (low/medium/high, default medium).
4. Data Models
Explicit database schema with field types, relationships, and constraints. AI generates much better code when it knows the data structure upfront.
Example:
Task: id (uuid), user_id (fk→users), title (varchar 100), status (enum: pending|in_progress|completed), created_at (timestamp)
5. User Flows
Step-by-step paths users take through the app. Include happy paths AND error states. AI uses these to structure component logic.
Example:
Login flow: Enter email → Enter password → [Success: redirect to dashboard | Failure: show error, allow 5 attempts then lock for 15 min]
6. Acceptance Criteria
Testable conditions that define 'done'. Written in Given/When/Then format that AI can use to generate tests alongside implementation.
Example:
Given a user is logged in, When they create a task without a title, Then show error 'Title is required' and prevent submission.
7. Explicit Non-Goals
What you are NOT building. This prevents AI from over-engineering or adding features you didn't ask for.
Example:
Non-goals: Team collaboration features, mobile apps (web only), integrations with external calendars, AI-powered task suggestions.
3Writing Acceptance Criteria AI Can Implement
Acceptance criteria are the most important part of your PRD for AI code generation. They provide concrete, testable conditions that AI can directly translate into code and tests. The Given/When/Then format works exceptionally well:
Good Acceptance Criteria
Given a user is on the login page
When they enter a valid email and correct password
Then they are redirected to /dashboard within 2 seconds
And a session cookie is set with 7-day expiry
And their last_login timestamp is updated in the database
Poor Acceptance Criteria
"User can log in successfully."
This tells AI nothing about session handling, redirects, error cases, or success indicators.
For every feature, write acceptance criteria for: the happy path (success), error states, edge cases (empty inputs, max lengths, special characters), and performance requirements. AI tools will use these to generate comprehensive implementations.
4Common Mistakes That Cause Poor Code Generation
Using vague language
Bad: "Make it user-friendly" or "should be fast"
Better: Define exact metrics: 'Form validation errors appear within 100ms of input' or 'Page loads in under 2 seconds on 3G connections'
Assuming industry standards
Bad: "Implement standard authentication"
Better: Specify exact requirements: bcrypt with 12 rounds, JWT with 1-hour expiry, refresh tokens stored in httpOnly cookies
Mixing requirements with implementation
Bad: "Use useState to track the form values"
Better: State the requirement, let AI choose implementation: 'Form state must persist during component re-renders and clear on successful submission'
Ignoring negative requirements
Bad: Only describing what should happen, not what shouldn't
Better: Add explicit constraints: 'Users cannot delete tasks created by other users even if they have the task ID'
Incomplete data relationships
Bad: "Tasks belong to projects"
Better: Define cascade behavior: 'When a project is deleted, all associated tasks are soft-deleted (status = archived, deleted_at = now)'
5PRD Template for AI Code Generation
Here's a simplified template you can adapt for your projects. The key is to be specific and explicit in every section:
# [Project Name] - Product Requirements Document ## 1. Overview **Problem:** [What problem does this solve?] **Target Users:** [Who is this for?] **Success Metrics:** [How do we measure success?] ## 2. Technical Constraints - **Stack:** [React 18, TypeScript 5.x, Tailwind CSS] - **Database:** [PostgreSQL via Supabase] - **Auth:** [Supabase Auth with email/password] - **Hosting:** [Vercel] - **Browser Support:** [Chrome, Firefox, Safari - last 2 versions] ## 3. Features ### Feature: [Feature Name] **Description:** [What does this feature do?] **Priority:** [P0/P1/P2] **User Story:** As a [user type], I want to [action] so that [benefit]. **Acceptance Criteria:** - Given [context], When [action], Then [result] - Given [context], When [error condition], Then [error handling] **Data Model:** ``` Table: feature_items - id: uuid (primary key) - user_id: uuid (foreign key → users) - name: varchar(100) NOT NULL - created_at: timestamp DEFAULT now() ``` **UI Requirements:** - [Component structure] - [Responsive behavior] - [Accessibility requirements] ## 4. Non-Goals - [What we are explicitly NOT building] - [Features to avoid scope creep] ## 5. Security Requirements - [Authentication requirements] - [Authorization rules] - [Data validation rules]
6Using Your PRD with AI Tools
Once you have a well-structured PRD, you can use it effectively with any AI coding tool:
With Cursor / Windsurf
Place your PRD in a docs/PRD.md file at your project root. The AI will reference it when generating code. Use @docs/PRD.md to explicitly include it in context.
With Claude Code
Reference your PRD at the start of a session: "Read the PRD in docs/PRD.md and implement the user authentication feature according to the acceptance criteria."
With Vibe Workflow
Our tool generates AI-optimized PRDs automatically. The output is structured specifically for AI consumption, including proper acceptance criteria, data models, and technical constraints.