196 lines
6.8 KiB
Markdown
196 lines
6.8 KiB
Markdown
# Adaptive PRD Template
|
|
|
|
## 🧠 Adaptive Vision
|
|
*"We're building a [product type] that learns and evolves with [user type] needs, starting with [initial hypothesis] and continuously adapting based on [feedback loops] to achieve [ultimate goal]."*
|
|
|
|
## 🔄 Learning Loops Framework
|
|
### Primary Learning Loop
|
|
```
|
|
User Action → Data Collection → Analysis → Hypothesis → Implementation → User Feedback → Refinement
|
|
```
|
|
|
|
### Secondary Learning Loops
|
|
- **Feature Loop**: [How features learn from usage]
|
|
- **User Loop**: [How user behavior informs product]
|
|
- **Market Loop**: [How market changes drive evolution]
|
|
|
|
## 🎯 Hypothesis-Driven Development
|
|
### Core Hypothesis
|
|
- **Primary Assumption**: [Main belief about user needs]
|
|
- **Success Criteria**: [How to validate the assumption]
|
|
- **Timeframe**: [When to evaluate]
|
|
- **Fallback Plan**: [What if assumption is wrong]
|
|
|
|
### Supporting Hypotheses
|
|
- **Hypothesis 1**: [Secondary assumption]
|
|
- **Test Method**: [How to validate]
|
|
- **Success Metrics**: [What success looks like]
|
|
- **Timeline**: [When to test]
|
|
|
|
- **Hypothesis 2**: [Secondary assumption]
|
|
- **Test Method**: [How to validate]
|
|
- **Success Metrics**: [What success looks like]
|
|
- **Timeline**: [When to test]
|
|
|
|
## 📊 Data-Driven Decision Framework
|
|
### Key Metrics (North Star + Supporting)
|
|
- **North Star Metric**: [Primary success indicator]
|
|
- **Leading Indicators**: [Early warning signals]
|
|
- **Lagging Indicators**: [Long-term success measures]
|
|
- **Health Metrics**: [System performance indicators]
|
|
|
|
### Feedback Collection Points
|
|
- **User Behavior**: [What users do]
|
|
- **User Feedback**: [What users say]
|
|
- **Business Metrics**: [Financial/operational data]
|
|
- **Market Signals**: [Competitive/industry trends]
|
|
|
|
## 🧪 Experimentation Strategy
|
|
### A/B Testing Framework
|
|
- **Test Categories**: [Types of experiments]
|
|
- **Success Criteria**: [How to measure results]
|
|
- **Statistical Significance**: [Confidence levels]
|
|
- **Rollout Strategy**: [How to implement winners]
|
|
|
|
### Feature Flags & Rollouts
|
|
- **Gradual Rollout**: [Percentage-based releases]
|
|
- **Cohort Testing**: [User group experiments]
|
|
- **Geographic Testing**: [Location-based tests]
|
|
- **Time-based Testing**: [Temporal experiments]
|
|
|
|
## 🔧 Adaptive Features
|
|
### Core Features (MVP)
|
|
- **Feature 1**: [Essential functionality]
|
|
- **Learning Mechanism**: [How it adapts]
|
|
- **Success Metrics**: [How to measure improvement]
|
|
- **Adaptation Triggers**: [When to change]
|
|
|
|
- **Feature 2**: [Essential functionality]
|
|
- **Learning Mechanism**: [How it adapts]
|
|
- **Success Metrics**: [How to measure improvement]
|
|
- **Adaptation Triggers**: [When to change]
|
|
|
|
### Intelligent Features
|
|
- **Personalization Engine**: [User-specific adaptations]
|
|
- **Recommendation System**: [Smart suggestions]
|
|
- **Automated Optimization**: [Self-improving systems]
|
|
|
|
## 📈 Evolution Roadmap
|
|
### Phase 1: Foundation (Months 1-3)
|
|
- **Goal**: [Establish core functionality]
|
|
- **Learning Focus**: [What to understand first]
|
|
- **Success Criteria**: [How to know it's working]
|
|
- **Adaptation Points**: [When to pivot]
|
|
|
|
### Phase 2: Intelligence (Months 4-6)
|
|
- **Goal**: [Add learning capabilities]
|
|
- **Learning Focus**: [What patterns to identify]
|
|
- **Success Criteria**: [How to measure intelligence]
|
|
- **Adaptation Points**: [When to enhance]
|
|
|
|
### Phase 3: Optimization (Months 7-12)
|
|
- **Goal**: [Maximize user value]
|
|
- **Learning Focus**: [What to optimize]
|
|
- **Success Criteria**: [How to measure optimization]
|
|
- **Adaptation Points**: [When to scale]
|
|
|
|
## 🔄 Continuous Improvement Process
|
|
### Weekly Review Cycle
|
|
- **Data Analysis**: [Review key metrics]
|
|
- **User Feedback**: [Analyze user input]
|
|
- **Hypothesis Validation**: [Check assumptions]
|
|
- **Adaptation Planning**: [Plan changes]
|
|
|
|
### Monthly Deep Dive
|
|
- **Trend Analysis**: [Long-term patterns]
|
|
- **Feature Performance**: [Success/failure review]
|
|
- **User Journey Mapping**: [Experience optimization]
|
|
- **Strategy Refinement**: [Adjust approach]
|
|
|
|
### Quarterly Strategy Review
|
|
- **Market Analysis**: [Competitive landscape]
|
|
- **User Research**: [Deep user understanding]
|
|
- **Technology Assessment**: [New capabilities]
|
|
- **Roadmap Adjustment**: [Future planning]
|
|
|
|
## 🛠️ Technical Architecture for Adaptation
|
|
### Data Infrastructure
|
|
- **Event Tracking**: [User action capture]
|
|
- **Analytics Pipeline**: [Data processing]
|
|
- **Real-time Monitoring**: [Live feedback]
|
|
- **Machine Learning Pipeline**: [Automated learning]
|
|
|
|
### Feature Management
|
|
- **Feature Flags**: [Toggle capabilities]
|
|
- **A/B Testing Platform**: [Experiment management]
|
|
- **Personalization Engine**: [User-specific features]
|
|
- **Recommendation System**: [Smart suggestions]
|
|
|
|
## 📊 Success Metrics Framework
|
|
### Learning Velocity
|
|
- **Hypothesis Testing Speed**: [How fast we learn]
|
|
- **Implementation Speed**: [How fast we adapt]
|
|
- **User Feedback Cycle**: [How fast we respond]
|
|
- **Market Adaptation**: [How fast we pivot]
|
|
|
|
### User Value Creation
|
|
- **User Satisfaction**: [How happy users are]
|
|
- **User Engagement**: [How much users use]
|
|
- **User Retention**: [How long users stay]
|
|
- **User Advocacy**: [How much users share]
|
|
|
|
### Business Impact
|
|
- **Revenue Growth**: [Financial success]
|
|
- **Cost Efficiency**: [Operational efficiency]
|
|
- **Market Position**: [Competitive advantage]
|
|
- **Scalability**: [Growth potential]
|
|
|
|
## 🚨 Adaptation Triggers
|
|
### Positive Triggers (Scale Up)
|
|
- **High User Engagement**: [When to expand features]
|
|
- **Strong User Feedback**: [When to accelerate]
|
|
- **Market Opportunity**: [When to invest more]
|
|
- **Competitive Advantage**: [When to double down]
|
|
|
|
### Negative Triggers (Pivot/Adjust)
|
|
- **Low User Engagement**: [When to change approach]
|
|
- **Poor User Feedback**: [When to fix issues]
|
|
- **Market Changes**: [When to adapt strategy]
|
|
- **Technical Limitations**: [When to rebuild]
|
|
|
|
## 🔮 Future Adaptation Vision
|
|
### Long-term Learning Goals
|
|
- **Predictive Capabilities**: [Anticipate user needs]
|
|
- **Automated Optimization**: [Self-improving systems]
|
|
- **Personalized Experiences**: [Individual user optimization]
|
|
- **Market Leadership**: [Industry innovation]
|
|
|
|
### Technology Evolution
|
|
- **AI/ML Integration**: [Intelligent features]
|
|
- **Real-time Processing**: [Instant adaptation]
|
|
- **Cross-platform Learning**: [Unified user experience]
|
|
- **Advanced Analytics**: [Deep insights]
|
|
|
|
## 📝 Success Criteria
|
|
### Learning Achievement
|
|
- [ ] Validated core hypothesis
|
|
- [ ] Established feedback loops
|
|
- [ ] Implemented adaptation mechanisms
|
|
- [ ] Achieved learning velocity targets
|
|
|
|
### User Value Delivery
|
|
- [ ] High user satisfaction scores
|
|
- [ ] Strong engagement metrics
|
|
- [ ] Positive user feedback
|
|
- [ ] Growing user base
|
|
|
|
### Business Success
|
|
- [ ] Achieved revenue targets
|
|
- [ ] Established market position
|
|
- [ ] Built competitive advantage
|
|
- [ ] Created sustainable growth
|
|
|
|
---
|
|
|
|
*This template emphasizes continuous learning and adaptation, ensuring the product evolves with user needs and market changes.*
|