AI-Powered Schema Assistant - PRD
AI-Powered Schema Assistant - Product Requirements Document
Section titled “AI-Powered Schema Assistant - Product Requirements Document”Executive Summary
Section titled “Executive Summary”The AI-Powered Schema Assistant is a conversational AI agent integrated into the Convex development environment that helps developers design, migrate, and optimize database schemas through natural language interactions. This feature addresses the complexity barrier that slows schema evolution and makes Convex more accessible to developers of all experience levels.
User Stories
Section titled “User Stories”Primary User Stories
Section titled “Primary User Stories”As a full-stack developer, I want to describe my data needs in plain English so that I can quickly create optimized schemas without deep Convex expertise.
As a team lead, I want automated migration planning so that I can confidently make schema changes without risking data loss or downtime.
As a new Convex user, I want interactive guidance so that I can learn best practices while building my first application.
As a senior developer, I want performance optimization suggestions so that I can proactively improve query efficiency.
Secondary User Stories
Section titled “Secondary User Stories”As a DevOps engineer, I want rollback-ready migrations so that I can safely deploy schema changes in production.
As a product manager, I want schema impact analysis so that I can understand the implications of feature requests.
Functional Requirements
Section titled “Functional Requirements”Core Capabilities
Section titled “Core Capabilities”1. Natural Language Schema Design
Section titled “1. Natural Language Schema Design”- Input: Natural language descriptions of data requirements
- Processing: Parse intent, map to Convex schema patterns, generate code
- Output: Valid Convex schema definitions with explanations
// Example interactionUser: "Create a user system with profiles and social connections"Assistant: Generated schema with users, profiles, and connections tables
2. Intelligent Migration Planning
Section titled “2. Intelligent Migration Planning”- Analysis: Compare current and target schemas
- Generation: Create step-by-step migration plans
- Validation: Check for potential data loss or conflicts
- Rollback: Generate reverse migration scripts
3. Interactive Schema Explorer
Section titled “3. Interactive Schema Explorer”- Visualization: Entity-relationship diagrams
- Navigation: Click-to-explore table relationships
- Insights: AI-generated optimization recommendations
- Documentation: Auto-generated schema documentation
4. Real-time Performance Optimization
Section titled “4. Real-time Performance Optimization”- Monitoring: Analyze query patterns in development
- Suggestions: Recommend indices, query improvements
- Validation: Test performance impact of changes
- Reporting: Performance improvement metrics
Integration Points
Section titled “Integration Points”Convex CLI Integration
Section titled “Convex CLI Integration”convex schema chat "Add user authentication system"convex schema migrate --interactiveconvex schema optimize --auto
IDE Extensions
Section titled “IDE Extensions”- VS Code extension with chat interface
- IntelliSense integration for schema suggestions
- Real-time validation and error highlighting
Convex Dashboard Integration
Section titled “Convex Dashboard Integration”- Schema assistant panel in web dashboard
- Visual schema designer with AI guidance
- Migration history and rollback interface
Technical Architecture
Section titled “Technical Architecture”System Components
Section titled “System Components”1. AI Engine
Section titled “1. AI Engine”- Language Model: Fine-tuned on Convex patterns and documentation
- Context Management: Maintains conversation state and project context
- Pattern Recognition: Identifies common schema design patterns
- Code Generation: Produces valid Convex schema and migration code
2. Schema Analysis Engine
Section titled “2. Schema Analysis Engine”- Parser: Analyzes existing schema definitions
- Dependency Tracker: Maps table relationships and query patterns
- Impact Calculator: Predicts effects of schema changes
- Performance Profiler: Identifies optimization opportunities
3. Migration Engine
Section titled “3. Migration Engine”- Plan Generator: Creates sequential migration steps
- Validation Engine: Checks migration safety and data integrity
- Rollback Generator: Creates reverse migration scripts
- Execution Monitor: Tracks migration progress and handles errors
4. Integration Layer
Section titled “4. Integration Layer”- CLI Interface: Command-line tools for development workflow
- IDE Extensions: Editor integrations for real-time assistance
- Dashboard API: Web interface for visual schema management
- Webhook System: Notifications and external tool integration
Data Models
Section titled “Data Models”Conversation Context
Section titled “Conversation Context”interface ConversationContext { projectId: string; currentSchema: SchemaDefinition; conversationHistory: Message[]; userPreferences: UserPreferences; activeSession: SessionMetadata;}
Schema Analysis
Section titled “Schema Analysis”interface SchemaAnalysis { tables: TableAnalysis[]; relationships: RelationshipMap; queryPatterns: QueryPattern[]; performanceMetrics: PerformanceData; optimizationSuggestions: Suggestion[];}
Migration Plan
Section titled “Migration Plan”interface MigrationPlan { steps: MigrationStep[]; rollbackPlan: RollbackStep[]; riskAssessment: RiskAnalysis; estimatedDuration: Duration; prerequisites: Prerequisite[];}
API Specifications
Section titled “API Specifications”Chat API
Section titled “Chat API”POST /api/schema/chat{ message: string; context: ConversationContext; projectId: string;}
Response: { reply: string; codeGenerated?: string; suggestions: Suggestion[]; followUpQuestions: string[];}
Migration API
Section titled “Migration API”POST /api/schema/migrate{ targetSchema: SchemaDefinition; options: MigrationOptions;}
Response: { plan: MigrationPlan; estimatedRisk: RiskLevel; rollbackStrategy: RollbackPlan;}
Analysis API
Section titled “Analysis API”GET /api/schema/analyze/{projectId}
Response: { analysis: SchemaAnalysis; optimizations: Optimization[]; healthScore: number;}
Security Considerations
Section titled “Security Considerations”Data Privacy
Section titled “Data Privacy”- Local Processing: Schema analysis happens locally when possible
- Encrypted Communication: All API calls use TLS 1.3
- Data Retention: Conversation history encrypted and auto-deleted
- Access Control: Integration with Convex project permissions
Code Safety
Section titled “Code Safety”- Validation: All generated code validated before execution
- Sandboxing: Migration testing in isolated environments
- Audit Logging: Complete history of schema changes and AI suggestions
- Rollback Protection: Automatic backups before migrations
AI Safety
Section titled “AI Safety”- Output Filtering: Prevent generation of harmful or incorrect code
- Confidence Scoring: AI indicates certainty levels for suggestions
- Human Review: Critical changes require explicit developer confirmation
- Fallback Modes: Graceful degradation when AI services unavailable
Performance Requirements
Section titled “Performance Requirements”Response Times
Section titled “Response Times”- Chat Responses: < 2 seconds for simple queries
- Code Generation: < 5 seconds for complex schemas
- Migration Planning: < 10 seconds for typical changes
- Schema Analysis: < 30 seconds for large projects
Scalability
Section titled “Scalability”- Concurrent Users: Support 1000+ simultaneous conversations
- Project Size: Handle schemas with 100+ tables efficiently
- Request Volume: Process 10,000+ API calls per minute
- Storage: Efficient conversation context storage and retrieval
Resource Usage
Section titled “Resource Usage”- Memory: < 512MB per active conversation
- CPU: Optimize for minimal impact on development experience
- Network: Compress responses and cache common patterns
- Storage: Efficient schema diff and analysis storage
Testing Strategy
Section titled “Testing Strategy”Unit Testing
Section titled “Unit Testing”- AI response validation and consistency
- Schema parsing and analysis accuracy
- Migration generation correctness
- Integration API functionality
Integration Testing
Section titled “Integration Testing”- End-to-end chat conversations
- CLI and IDE extension workflows
- Dashboard integration scenarios
- Multi-project context handling
Performance Testing
Section titled “Performance Testing”- Response time benchmarking
- Concurrent user load testing
- Large schema analysis performance
- Migration execution time validation
User Acceptance Testing
Section titled “User Acceptance Testing”- Developer workflow usability
- Schema design accuracy validation
- Migration safety and rollback testing
- Learning curve and onboarding effectiveness
Success Criteria
Section titled “Success Criteria”Technical Metrics
Section titled “Technical Metrics”- 95% migration success rate with zero data loss
- < 3 second average response time for chat interactions
- 90% code generation accuracy requiring no manual fixes
- < 1% false positive rate for optimization suggestions
User Experience Metrics
Section titled “User Experience Metrics”- 8/10 average satisfaction rating from developer surveys
- 50% reduction in schema-related development time
- 80% of developers using the assistant for schema work
- 90% feature adoption within teams that try it
Business Impact
Section titled “Business Impact”- 40% faster new developer onboarding to Convex
- 60% reduction in schema-related support tickets
- 25% improvement in query performance across projects
- 15% increase in Convex customer satisfaction scores
Implementation Timeline
Section titled “Implementation Timeline”Phase 1: MVP (Months 1-3)
Section titled “Phase 1: MVP (Months 1-3)”- Basic chat interface with schema generation
- Simple migration planning
- CLI integration
- Core AI model training
Phase 2: Enhanced Features (Months 4-6)
Section titled “Phase 2: Enhanced Features (Months 4-6)”- Visual schema explorer
- Performance optimization suggestions
- IDE extensions (VS Code)
- Dashboard integration
Phase 3: Advanced Capabilities (Months 7-9)
Section titled “Phase 3: Advanced Capabilities (Months 7-9)”- Complex migration scenarios
- Multi-project context awareness
- Advanced performance analytics
- Team collaboration features
Phase 4: Optimization & Scale (Months 10-12)
Section titled “Phase 4: Optimization & Scale (Months 10-12)”- Performance improvements
- Advanced AI capabilities
- Enterprise features
- Community pattern learning
Risk Mitigation
Section titled “Risk Mitigation”Technical Risks
Section titled “Technical Risks”- AI Accuracy: Extensive testing and human review systems
- Performance Impact: Careful resource management and caching
- Integration Complexity: Phased rollout with feature flags
- Data Security: Comprehensive security audit and encryption
Adoption Risks
Section titled “Adoption Risks”- Learning Curve: Interactive tutorials and onboarding guides
- Trust Building: Transparent AI confidence indicators
- Change Management: Gradual feature introduction
- Support Load: Comprehensive documentation and training
Business Risks
Section titled “Business Risks”- Development Cost: Phased approach with early value delivery
- Market Timing: Regular user feedback and iteration cycles
- Competition: Focus on Convex-specific advantages
- Resource Allocation: Cross-team collaboration and shared ownership
This PRD provides the foundation for building an AI-powered schema assistant that transforms how developers work with Convex databases, making complex schema management accessible, safe, and efficient.