Bulletproof PRD Template

Bulletproof PRD Template
Product Requirements Document for AI Systems
Overview
A PRD (Product Requirements Document) is your blueprint. It's your clear thinking written down. A good PRD prevents building the wrong thing, scope creep, expensive rebuilds, and "that's not what I meant" moments.
Prevents Wrong Builds
Clear requirements stop you from building features nobody needs
Stops Scope Creep
Defined boundaries keep projects on track and on budget
Avoids Expensive Rebuilds
Get it right the first time instead of costly do-overs
Eliminates Confusion
Everyone knows exactly what you're building and why
Problem Statement, Goals & User Stories
1. Problem Statement
What problem are you solving?
Be specific
State the problem, not symptoms
Who has this problem?
Example: "Employees waste an average of 2 hours per day searching through company documents to find information. This reduces productivity and leads to inconsistent answers."
2. Goals
What are you trying to achieve?
Primary goal
Secondary goals
Success criteria
Example:
Primary: Reduce time to find information by 70%
Secondary: Provide consistent, accurate answers
Success: < 30 seconds to get answer, > 80% accuracy
3. User Stories
Who uses this and why?
As a [user type], I want [goal] so that [benefit]
Example:
As an employee, I want to ask "What's our vacation policy?" and get an accurate answer in under 30 seconds
As an admin, I want to upload new documents and have them automatically available in the system
Functional & Non-Functional Requirements
4. Functional Requirements
What must it do?
Core features
User interactions
System behaviors
Example:
Users can ask questions in natural language
System searches company documents
System provides answers with source citations
Admins can upload documents (PDF, Word, text)
System processes documents automatically
5. Non-Functional Requirements
How well must it work?
Performance (response time, throughput)
Scalability (users, data volume)
Reliability (uptime, error handling)
Security (access control, data protection)
Example:
Response time: < 3 seconds for 95% of queries
Scalability: Support 1,000 concurrent users
Uptime: 99.5% availability
Security: Role-based access control
Technical & Data Requirements
6. Technical Requirements
What technology is needed?
AI models/APIs
Databases
Infrastructure
Integrations
Example:
Vector database (Pinecone or similar)
Embedding API (OpenAI)
LLM API (OpenAI GPT-3.5)
Document processing pipeline
Authentication system integration
7. Data Requirements
What data is needed?
Input data
Data sources
Data format
Data volume
Example:
Input: Company documents (PDF, Word, text)
Sources: Document storage (S3, Google Drive)
Format: Text extractable from documents
Volume: 10,000+ documents initially
User Interface & Success Metrics
8. User Interface Requirements
What does the UI need?
Key screens
User interactions
Design considerations
Example:
Chat interface for questions
Document upload interface
Answer display with sources
Mobile-responsive design
9. Success Metrics
How do you measure success?
Key metrics
Target values
Measurement method
<3s
Average Response Time
Target for query responses
>80%
Answer Accuracy
Target accuracy rate
>4/5
User Satisfaction
Target star rating
70%
Time Saved
Target reduction in search time
Constraints & Risks
10. Constraints
What are the limitations?
Budget
Timeline
Technical constraints
Business constraints
Example:
Budget: $500/month for AI APIs
Timeline: Launch in 3 months
Technical: Must work with existing auth system
Business: Must comply with data privacy regulations
11. Risks and Mitigations
What could go wrong?
Technical risks
Business risks
Mitigation strategies
Example:
Risk: AI costs exceed budget
Mitigation: Implement caching, use GPT-3.5
Risk: Low answer accuracy
Mitigation: Fine-tune retrieval, improve chunking
Risk: Slow response times
Mitigation: Cache common queries, optimize database
Platform Selection & Complete Example
12. Platform Selection
Which platform/framework?
Web: Next.js vs Vite
Mobile: Expo vs PWA
Reasoning
Example:
Platform: Next.js
Reason: Need API routes for document processing and chat
Alternative considered: Vite (rejected - no API routes)
Example: Complete PRD
01
Problem
Employees waste 2 hours/day searching documents
02
Goals
Reduce search time by 70%, provide accurate answers, support 1,000 users
03
User Stories
Employee: Ask questions, get answers. Admin: Upload documents, manage system
04
Functional Requirements
Natural language questions, document search, answer with citations, document upload
05
Non-Functional Requirements
< 3s response time, 99.5% uptime, support 1,000 users
06
Technical Requirements
Vector DB (Pinecone), OpenAI APIs, document processor
07
Data Requirements
10,000+ documents, PDF, Word, text formats
08
UI Requirements
Chat interface, upload interface, mobile-responsive
09
Success Metrics
< 3s response, 80% accuracy, 4/5 satisfaction
10
Constraints
$500/month budget, 3 month timeline
11
Risks
Cost overrun → Caching, Low accuracy → Fine-tune, Slow response → Optimize
12
Platform
Next.js (need API routes)
Common Mistakes & AI-Specific Considerations
Common Mistakes
1
Vague requirements
"Make it good" isn't specific
2
No success metrics
Can't measure success
3
Ignoring constraints
Unrealistic expectations
4
No risk planning
Surprised by problems
5
Skipping sections
Missing critical information
AI-Specific Considerations
For AI Systems, Also Include:
AI Model Selection
Which models and why
Confidence Thresholds
When to use AI vs. fallback
Error Handling
What if AI fails?
Cost Projections
AI API costs
Data Privacy
How is data handled?
Bias Considerations
How to prevent bias?
How to Use & Next Steps
How to Use
Fill out every section
Don't skip
Be specific
Vague = problems later
Get feedback
Review with stakeholders
Iterate
PRD is living document
Reference it
Use during development
Next Steps
After completing PRD:
Review with team
Get stakeholder approval
Create architecture design
Start development
Reference PRD throughout build