Best Practices for Product Planning
Proven strategies and techniques for getting the most out of AI Product Manager.
Input Quality & Planning
Write Detailed Product Descriptions
The quality of AI output directly correlates with the quality of your input.
→Start with a clear problem statement: What problem does this product solve?
→Define target audience: Who are the primary and secondary users?
→List key features: What core capabilities must it have?
→Mention constraints: Any technical, budget, or time constraints?
→Add context: Competitive landscape, market opportunity, business goals
Include Domain Expertise
Leverage your team's knowledge to guide AI generation.
→Mention technology preferences: Preferred tech stack, frameworks, databases
→Reference existing systems: How does this integrate with current infrastructure?
→Add business requirements: Revenue model, scaling expectations, SLAs
→Include user journey: How do users interact with the product?
→Note edge cases: Any special scenarios or corner cases to handle
Provide Examples
Concrete examples make requirements much clearer to the AI.
→Include user stories with specific scenarios
→Reference similar products or features
→Provide sample data formats or interfaces
→Describe typical workflows with steps
→Share design mockups or wireframes if available
Model Selection & Configuration
Choose Based on Your Priorities
Different models excel at different things.
→Claude: Best for complex reasoning and nuanced requirements (highest quality)
→Groq: Best for speed (50-70% faster than others, minimal quality loss)
→OpenRouter: Best for flexibility (access multiple models, price optimization)
→HuggingFace: Best for cost sensitivity (most economical option)
Test Multiple Models
What works best varies by project type and team preferences.
→Start with Claude to see best-case quality output
→Test Groq for speed comparison and cost savings
→Try OpenRouter if you have specific model preferences
→Run A/B tests on small projects before committing to one model
→Keep notes on what worked best for different project types
Experiment with Parameters
Fine-tune model behavior for your use case.
→Temperature: Lower (0.3-0.5) for consistency, higher (0.8+) for creativity
→Max tokens: Higher for detailed outputs, lower for quick summaries
→Top-K sampling: Affects creativity and diversity of suggestions
→Start with defaults and adjust based on results
Workflow Optimization
Use Iterative Refinement
Don't expect perfection on first generation.
→Generate → Review → Customize → Regenerate is a valid workflow
→Use version history to compare different approaches
→Start broad (full project) then refine specific sections
→Test regenerating just the weak sections instead of everything
→Keep iterations focused on high-impact improvements
Collaborate Early with Team
Get expert feedback to improve output quality.
→Share generated PRD within 24 hours for team feedback
→Have engineers review architecture and timeline estimates
→Have designers review UX/interface assumptions
→Get stakeholder sign-off before finalizing
→Document any customizations made by team for next iteration
Manage Scope Effectively
Break large projects into manageable pieces.
→For complex products: Generate in phases (MVP first, then enhancements)
→For large teams: Generate per component or functional area
→For tight timelines: Focus on PRD and tasks, skip architectural deep-dive
→Always prioritize based on dependencies and critical path
→Consider team capacity when planning timelines
Quality Assurance
Validate Timeline Estimates
AI estimates are a starting point, not gospel.
→Have your engineering lead review all time estimates
→Adjust for team experience level (junior teams need more time)
→Account for team size and availability
→Consider technology familiarity (new tech = longer development)
→Add 15-20% buffer for unknowns and scope creep
→Use historical project data to calibrate estimates
Review Acceptance Criteria
Clear acceptance criteria are essential for quality delivery.
→Ensure criteria are testable (not subjective)
→Include both happy path and edge cases
→Make sure criteria align with business goals
→Get QA team input on completeness
→Link criteria to actual product behavior, not implementation details
Verify Technical Recommendations
Architecture suggestions should match your context.
→Review technology choices for team expertise alignment
→Check that recommendations scale to your projected size
→Verify integration points with existing systems
→Ensure security and compliance requirements are addressed
→Have senior engineers validate architectural decisions
Version Control & Documentation
Maintain Version History
Keep records of your planning evolution.
→Create snapshots before major regenerations
→Use version labels for important milestones (MVP Plan, V1.0, etc.)
→Document why regenerations happened (to aid future decisions)
→Compare versions to see impact of different inputs
→Archive old versions but keep latest active
Document Customizations
Record changes made to AI-generated content.
→Keep a changelog of modifications by team
→Note why changes were made (business reasons, technical constraints)
→Document any deviations from AI suggestions and why
→This helps refine inputs for next projects
→Share learnings with team for consistent approach
Integration & Handoff
Export in Right Format
Choose export format based on how you'll use it.
→For Jira/Linear: Use direct integration for seamless syncing
→For presentation: Export as PDF with nice formatting
→For editing: Markdown or Word for maximum flexibility
→For archival: JSON for complete data preservation
→For sharing: Read-only links or PDFs for stakeholders
Sync with Project Management Tools
Reduce manual data entry and keep everything in sync.
→Use built-in integrations with Jira, Linear, Asana
→Sync tasks automatically instead of copy-paste
→Keep timeline and estimates updated across platforms
→Use labels/tags for easy filtering and tracking
→Set up webhooks for bidirectional sync
Create Development Handoff
Make it easy for engineering to start work.
→Create acceptance criteria for each task (not just summary)
→Include example inputs/outputs where helpful
→Reference architecture diagram for context
→Add acceptance test scenarios for clarity
→Assign tasks and estimate story points
Pro Tips & Shortcuts
Batch Processing
Generate multiple task batches at once for parallel processing
Template System
Save your customized PRD structure as a template for future projects
Partial Regeneration
Only regenerate weak sections instead of the entire document
Model Switching
Regenerate same input with different models to compare outputs
Team Feedback Loop
Use comments and suggestions feature to gather input before finalizing
Integration Automation
Set up webhooks to auto-sync changes to your project management tool